Calabuig Serna, Tono Martínez Rodero, Iris

Segarra Martín, Eva

DE NOVO RNA-SEQ FOR THE

STUDY OF ODAP SYNTHESIS

PATHWAY IN LATHYRUS

SATIVUS

Escola Tècnica Superior d’Enginyeria Agronòmica i del Medi Natural

Universitat Politècncia de València

May, 2015

INDEX

page

1. INTRODUCTION 1

- L. SATIVUS CONTEXT 1

- OUR AIM 1

2. THE FIRST APPROACH TO THE PROJECT 2

- HOW L. SATIVUS AND ODAP ARE RELATED 2

- EXPRESSION STUDY BUT, WHICH TECHNOLOGY? 2

- MICROARRAYS: WHY NOT 2

- OUR CHOICE: DE NOVO RNAseq 3

3. EXPERIMENTAL DESIGN 3

3.1. SAMPLE RECOVERY 3

3.2. RNAseq ASSAY 4

3.3. DATA PROCESSING 4

i. DATA FILTERING 4

ii. TRANSCRIPTOME ASSEMBLY 4

3.4. DATA ANALYSIS 5

4. BUDGET ESTIMATE 7

5. CONCLUSIONS 7

6. REFERENCES 8

1

ABSTRACT

Lathyrus sativus frequently becomes the main survival nourishment in areas where drought

and famine are frequent. However, it is associated with lathyrism development when it is

consumed during large periods due to its high content of β, N-oxalyl-L-α, β-diaminopropinoic

acid (β-ODAP). The aim of this project is to recognize the genes fostering β-ODAP biosynthesis

through de novo RNAseq assay, as a foundation for a future low β-ODAP content grass pea

variant.

KEYWORDS: Lathyrus sativus, ODAP, de novo RNAseq, Trinity, ANOVA

1. INTRODUCTION

L. sativus context

Lathyrus sativus –commonly known as grass pea, is a plant belonging to Fabaceae family. It

is considered as an ‘insurance crop’ due to its advantageous biological and agronomical

characteristics: both flooding and drought anaerobic conditions withstanding; resistance to

insects and pests; nitrogen fixation; high grain-yielding capacity and high protein content of

the seed (Yan et al., 2006). In areas that are prone to drought and famine like Asia and East

Africa, this legume produces considerable yields when all other crops fail (Oudhia, 1999),

becoming the main harvest of subsistence agriculture (Spencer et al., 1986).

The problem is that its seeds contain a neuro-excitatory amino acid called β, N-oxalyl-L-α,

β-diaminopropinoic acid (β-ODAP) which is thought to produce a neurodegenerative

disease when these grains are consumed as the main source of proteins for a prolonged

period of time (3-4 months) (Yan et al., 2006) . The sickness consists on lathyrism or

neurolathyrism, which is manifested as an irreversible paralysis of the lower limbs affecting

humans and domestic animals when they consume Lathyrus sativus or allied species

containing β-ODAP (Spencer et al., 1986).

Our aim

Grass pea is a promising crop for adaptation under climate change because of its tolerance

to drought, water-logging and salinity and for being almost free from insect-pests and plant

diseases. In spite of such virtues, global area under its cultivation has decreased because of

ban on its farming in many countries due to its association with neurolathyrism (Xing et al.,

2000).

Therefore, it seems necessary to invest on Lathyrus sativus genetic improvement through

conventional and biotechnological tools to make this survival food safe for human

consumption. It is reasonable given that several studies have revealed that β-ODAP can be

brought down without affecting its yield and stability (Kumar et al., 2011).

For all that previously exposed and continuing with the already existing expectations in this

issue, our aim is to design a genomics project in order to understand the cellular pathways

controlling β-ODAP biosynthesis through a global expression analysis. The results of this

study would ideally establish the basis for a future improvement of grass pea with very low

amounts of β-ODAP.

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2. OUR FIRST APPROCH TO THE PROJECT

How L.sativus and ODAP synthesis are related

Both genotype and environment are known to affect β-ODAP concentration (Hanbury et al.,

1999), but how each of them contributes or if any possible environment interactions exist

was poorly analysed at first.

Then it has been researched and what has been found is that genotype is the most

important determinant –as β-ODAP content is a polygenic trait (Long et al., 1996).

Environment has less influence although it still remains significant and genotype-

environment interactions have no effect on seed β-ODAP concentrations (Yan et al., 2006).

Regarding to genotype the following conclusions were reported: diversity of β-ODAP

concentration between different species as L. cicera produces less than L.sativus (Hanbury

et al., 1999). Moreover, Hanbury et al. (2000) stated that there are high-toxin variety

‘Jamalpur’ and low-toxin variety ‘LS 8603’.

Focusing on environmental influences several data was found: high temperatures induce

the isomerization of naturally occurring β-ODAP (concentration about 95% of the total

ODAP content in L.sativus) to α-ODAP (Long et al., 1996) –being the α-isomer less toxic (De

Bruyn et al., 1994), nitrogen and phosphorus fertilizers may help decrease β-ODAP content

(Jiao et al., 2011) and cadmium in the soil has been related with β-ODAP concentration

raise (Yaozu et al., 1992). Because lathyrism frequently occur during drought periods, the

change in content of β-ODAP in seeds of grass pea under drought conditions has been of

particular concerns: augmentation of β-ODAP content has been observed under prolonged

water stress (Patto et al., 2006), which implies abscisic acid (ABA) accumulation –pointed

out to affect too (Xing et al., 2000). Furthermore, Kumar et al. (2011) published that water

stress can double β-ODAP level.

Development stages have been found to affect β-ODAP concentration too. Although it is

present in all tissues at all development stages, there are two peaks: the maximum

concentration has been noticed in young seedlings (Long et al., 1996) and seed embryo (De

Bruyn et al., 1994), while remains low in all tissues and organs since the early vegetative

phase (Jiao et al., 2011). The lower peak occurs at the reproductive stage and in ripening

seeds (Yaozu et al., 1992).

Expression study but, which technology?

Our immediate idea was to use microarray technology to address the global expression

analysis. When we contacted Agillent we realised that L. sativus genome was needed to

design the array.

Microarrays: why not

The next step was to look for L. sativus species in the NCBI data base to check if any

information was available. The results were: 178 ESTs, 109 genes, 123249 DNA&RNA

sequences, 99 clusters of expressed transcripts, 238 proteins and 50 protein cluster entries,

but 0 assemblies. We even tried with Lathyrus genus and the same was obtained. That

meant we did not have any information about its complete genome.

In that situation we thought of including in our project previous steps of sequencing and

annotation. However, the other alternative for expression analysis came out: RNAseq.

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Our choice: de novo RNAseq

Next generation RNA sequencing (RNAseq) is rapidly replacing microarrays as the

technology of choice for whole-transcriptome studies. RNAseq also provides a far more

precise measurement of levels of transcripts and their isoforms than other methods, as

Yang T. et al., (2014) affirm. Once we deliberated this option, we worried about the need of

another time having L.sativus genome sequence available.

Nevertheless, the definitive solution appeared: doing an RNAseq assay without reference

genome. We searched for any previous similar experiments and we found several ones,

which were enough to support our choice (Grabherr et al., 2011; Li and Dewey, 2011; Li et

al., 2014).

Finally we decided de novo RNAseq was going to be our procedure and we could start to

think in the experimental design.

3. EXPERMIENTAL DESIGN

3.1. SAMPLE RECOVERY

We decided to combine three different aspects of L. sathivus in order to select the

samples we would work with. Our objective was to take advantage of previous

knowledge about when ODAP synthesis occurs.

The first criterion was differentiating between tissues –and development stages: L.

sativus seeds contain higher amount of β-ODAP than the stem (Yan et al., 2006). We also

selected the two varieties ‘Jamalpur’ and ‘LS-8603’, knowing that the first one shows

higher levels of neurotoxin (Hanbury et al., 2000). The third consideration was to growth

the samples under two environmental conditions for which we known ODAP

concentration would be different: drought and control (Patto et al., 2006 and Kumar et

al., 2011). Water deficit stress would be induced by 20% PEG (polyethylene glycol)

treatment for 5 days (Jiang et al., 2013).

We settled not to make replicates of each sample basing on Marioni et al. (2008)

statement: ‘We find that the Illumina sequencing data are highly replicable, with

relatively little technical variation, and thus, for many purposes, it may suffice to

sequence each mRNA sample only once’.

As a result, we suggested the following combinations of samples:

For samples 1, 2, 5 and 6, 50 g of seeds (Pañeda et al., 2001) would be collected in order

to obtain enough RNA in the following RNA extraction step.

For samples 3, 4, 7 and 8 around 100 mg of tissue would be taken (Brunet et al., 2009).

Sample Variety Tissue Environmental conditions

1 ‘Jamalpur’

Seed Drought

2 Control

3 Stem Drought

4 Control

5 ‘LS-8603’

Seed Drought

6 Control

7 Stem Drought

8 Control

Table I. Combination of variety, tissue and environmental conditions in the 8 samples

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3.2. RNAseq ASSAY

To perform RNA extraction from each one of the samples, we based on the report of

Yang Z.B et al. (2014), who worked with the model specie Arabidopsis thaliana.

Total RNA would be isolated using RNeasy Plant Mini Kit (Qiagen), which was used also

by Skiba et al., (2005) in L. sativus. DNA would be removed by treating the samples with

DNase I. mRNA from samples would be enriched using oligo(dT) magnetic beads. Mixed

with the fragmentation buffer, the mRNA would be chopped into fragments of 200 bp.

The first strand of cDNA would be synthesized by using random hexamer primer and for

the second strand buffer; deoxynucleotide triphosphates (dNTPs), RNase H and DNA

polymerase I would be added. Double-stranded cDNA would be purified and sequencing

adaptors would be ligated to the fragments, which were amplified by PCR. The

constructed libraries for each sample would be qualified and quantified with an Agilent

2100 Bioanaylzer and the ABI StepOnePlus Real-Time PCR System and finally sequenced

via Illumina HiSeq 2000.

3.3. DATA PROCESSING

Data filtering

Once we would obtain the raw data, we followed the data filtering method described by

Pan et al. (2015). To avoid the effect of sequencing errors when performing the

assembly we would remove the following reads:

- Those which had adapter sequences in order not to infer in the assembly of the

real transcript.

- The ones with low quality at the ends of the reads to avoid any assembly

problem due to possible technological errors of the sequencing platform used.

- Reads with an average quality score lower than 15 in Phred. Phred quality scores

are assigned to each nucleotide base call in automated sequencer traces.

- Single reads less than 36 bp, as they were considered too short.

Transcriptome assembly

Owing to there is no reference genome of Lathyrus sativus, we had to look for

bibliography where specific bioinformatic tools had been used for de novo assembly of

full-length transcripts. First we believed on Whang et al. (2010) work, as they used the

software SOAPdenovo to achieve assembly and characterization of root transcriptome in

Ipomoea batatas. However, we found Trinity method described by Grabherr et al.

(2011), which consists on the reconstruction of a large fraction of transcripts, including

alternatively spliced isoforms and transcripts from recently duplicated genes. Compared

with other de novo transcriptome assemblers, Trinity recovers more full-length

transcripts across a broad range of expression levels with sensitivity similar to methods

that rely on genome alignments.

Trinity has three modules: Inchworm, Chrysalis and Butterfly, applied sequentially to

process large volumes of RNA-seq reads.

Inchworm efficiently reconstructs linear transcript contigs in six steps.

1. Constructs a k-mer dictionary from all sequence reads.

2. Removes likely error-containing k-mers from the k-mer dictionary.

3. Selects the most frequent k-mer in the dictionary to seed a contig assembly,

excluding both low-complexity and singleton k-mers (appearing only once).

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4. Extends the seed in each direction by finding the highest occurring k-mer with a

k − 1 overlap with the current contig terminus and concatenating its terminal

base to the growing contig sequence (once a k-mer has been used for extension,

it is removed from the dictionary).

5. Extends the sequence in either direction until it cannot be extended further.

Then reports the linear contig.

6. Repeats steps 3–5, starting with the next most abundant k-mer, until the entire

k-mer dictionary has been exhausted.

Chrysalis clusters minimally overlapping Inchworm contigs into sets of connected

components, and constructs complete de Bruijn graphs for each component. Each

component defines a collection of Inchworm contigs that are likely to be derived from

alternative splice forms or closely related paralogs.

1. It recursively groups Inchworm contigs into connected components. Contigs are

grouped if there is a perfect overlap of k − 1 bases between them and if there is

a minimal number of reads that span the junction across both contigs with a

(k − 1)/2 base match on each side of the (k − 1)-mer junction.

2. It builds a de Bruijn graph for each component using a word size of k − 1 to

represent nodes, and k to define the edges connecting the nodes. It weights

each edge of the de Bruijn graph with the number of k-mers in the original read

set that support it.

3. It assigns each read to the component with which it shares the largest number of

k-mers, and determines the regions within each read that contribute k-mers to

the component.

Butterfly reconstructs plausible, full-length, linear transcripts by reconciling the

individual de Bruijn graphs generated by Chrysalis (with the original reads and paired

ends. It reconstructs distinct transcripts for splice isoforms and paralogous genes, and

resolves ambiguities stemming from errors or from sequences >k bases long that are

shared between transcripts. Butterfly consists of two parts:

1. Graph simplification: Butterfly iterates between merging consecutive nodes in

linear paths in the de Bruijn graph to form nodes that represent longer

sequences.

2. Pruning edges that represent minor deviations (supported by comparatively few

reads), which likely correspond to sequencing errors.

3.4. DATA ANALYSIS

Transcriptome annotation

The next step would be to identify which genes would contain our assembled

transcriptome. To achieve it we followed the Pan et al. (2015) indications, who did de

novo RNAseq although they worked with animal samples. However, Yang Z.B et al.

(2014) worked with A. thaliana following the same approach to annotate.

A BLASTx alignment would be performed between the transcripts and several protein

databases: the NCBI non-redundant protein database, Swiss-Prot and the Kyoto

Encyclopedia of Genes and Genomes (KEGG) pathway database. The best hits would

determine the transcription direction and coding region of transcripts.

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The order of prioritization of databases to select sequence direction would be: NCBI

non-redundant protein database, Swiss-Prot and KEGG.

When a transcript could not have predict a coding sequence (CDS) using a homology

method, the software ESTScan would be used as the alternative for prediction. ESTScan

is a program that can detect coding regions in DNA sequences even if they are of low

quality.

Once CDS would be determined for each transcript, the peptide sequences would be

translated using those CDS with lengths larger than 100 bp.

In addition, the transcripts would be annotated with NCBI non-redundant nucleotide

database using BLASTn. Following these results to annotate the transcripts with GO

terms, Blast2GO would be used to obtain GO entries according to molecular function,

biological process and cellular component ontologies.

Differential expression

To obtain expression profiles we also followed Pan et al. (2015) methods. BWAaligner

(Burrows-Wheeler Aligner), which is a software package for mapping low-divergent

sequences against a large reference genome, would map the reads back to the already

assembled transcripts. Each transcript would be normalized into FPKM values

(Fragments Per kb per Million Fragments).

FPKM = total fragments

mapped reads (millions)* exon length (Kb)

Taking into account that there would be differentially expressed genes due to the

different variables implied in the choice of the 8 samples (varieties, stages of

development and environmental conditions), seems reasonable to suppose that the

genes implied in ODAP biosynthesis would be those commonly overexpressed in the

conditions expected to induce more ODAP production. The combination of ‘Jamalpur’

variety, seed tissue and drought condition are those thought to increase the synthesis of

β-ODAP by L.sativus.

In order to detect differentially expressed genes among the combinations of variables

previously described, all expression data of the 8 samples were subjected to ANOVA

analysis following a 23 factorial design (Box et al., 1978) using StatGraphics software

(Forner-Giner et al., 2010). In our case the considered factors were variety, tissue and

environmental conditions, with two levels for each factor. The levels for each factor

would be:

- Factor variety

o ‘Jamalpur’ (+)

o ‘LS-8603’ (-)

- Factor tissue

o seed (+)

o stem(-)

- Factor environmental condition

o drought(+)

o control(-)

Variety Tissue E. condition

1 + + +

2 + + -

3 + - +

4 + - -

5 - + +

6 - + -

7 - - +

8 - - -

Table II. 23 Factorial design

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The ANOVA analysis would provide a list of differentially expressed genes for each one

of the samples. As we previously knew, sample 1 gathers the proper conditions to

contain more ODAP. ANOVA statistical test takes into account the possible effect of all

the level combinations over the response variable (expression level for each gene).

Provided that, the genes showing a significantly higher expression in sample 1 than in

the other samples would be the candidate genes for being responsible of ODAP

synthesis.

As all the sample transcripts would have been assembled using Trinity software and

annotated afterwards, the genes detected by ANOVA could be identified.

4. BUDGET ESTIMATE

RNAseq assay

We had 8 different samples from which we would have extracted the RNA. Then, we would

have bought an RNA kit extraction large enough to cover our number of samples. In Qiagen

website was necessary to be logged in to access price information, so we contacted with

Life Science sales department. They informed us that the cost of each extraction, using

RNeasy Mini Kit (Qiagen), would be 150$ per sample. As we had 8 different samples, the

total price would be about 1,200$.

They also told us that when using HiSeq Illumina technology, the price of 20 million reads

would be 1,200 $ per sample. As there is not any RNAseq experiment published with

Lathyrus sativus, we estimated the price by looking in the work of Wang et al. (2010), who

obtained 60,000,000 raw sequencing reads from an RNAseq assay of Ipomoea batatas root.

Sequencing the transcriptome of each sample would cost around 3,600$.

As we would have 8 samples, the total cost of RNA extraction and RNAseq would be

30,000$.

Softwares

Trinity software can be downloaded for free (http://trinityrnaseq.github.io/).

ESTScan is an on-line available tool (http://myhits.isb-sib.ch/cgi-bin/estscan).

BWAaligner can be also downloaded from its website (http://bio-bwa.sourceforge.net/).

Finally, the total cost of the project would be approximately 30,000$.

5. CONCLUSIONS

With the obtained results, this project would contribute to the knowledge of Lathyrus

sativus and the genes involved in ODAP biosynthesis. As we previously exposed, further

investigations may provide a deeper insight into the possible modification of the specie to

achieve a lower ODAP content variant.

Regarding personal aspects as students, this project has meant for us the first time of

dealing with the development of a study, since the very early steps –as looking for

references to support our ideas. We have invested more time, more effort and more

commitment than in any previously work, and it has provided us a more realistic idea of

how proceed in science –adding also more confidence and more skills than we had before.

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