L.sativus and ODAP

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.

2
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.

3
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

4
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).

5
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.

6
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. 2
3
Factorial design

7
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.

8
6. REFERENCES
Box, G. E., Hunter, W. G., & Hunter, J. S. (1978). Chapter 5: factorial designs at two levels.
Statistics for experimenters.
Brunet, J., Varrault, G., Zuily-Fodil, Y., & Repellin, A. (2009). Accumulation of lead in the
roots of grass pea (Lathyrus sativus L.) plants triggers systemic variation in gene expression
in the shoots. Chemosphere, 77(8), 1113-1120.
De Bruyn, A., Becu, C., Lambein, F., Kebede, N., Abegaz, B., & Nunn, P. B. (1994). The
mechanism of the rearrangement of the neurotoxin β-ODAP to α-ODAP. Phytochemistry,
36(1), 85-89.
Forner-Giner, M. A., Llosá, M. J., Carrasco, J. L., Perez-Amador, M. A., Navarro, L., & Ancillo,
G. (2010). Differential gene expression analysis provides new insights into the molecular
basis of iron deficiency stress response in the citrus rootstock Poncirus trifoliata. Journal of
experimental botany, 61(2), 483-490.
Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., ... & Regev,
A. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference
genome. Nature biotechnology, 29(7), 644-652.
Hanbury, C. D., Siddique, K. H. M., Galwey, N. W., & Cocks, P. S. (1999). Genotype-
environment interaction for seed yield and ODAP concentration of Lathyrus sativus L. and
L. cicera L. in Mediterranean-type environments. Euphytica, 110(1), 45-60.
Hanbury, C. D., White, C. L., Mullan, B. P., & Siddique, K. H. M. (2000). A review of the
potential of Lathyrus sativus L. and L. cicera L. grain for use as animal feed. Animal Feed
Science and Technology, 87(1), 1-27.
Jiang, Jinglong, et al. "Correlation of drought resistance in grass pea (Lathyrus sativus) with
reactive oxygen species scavenging and osmotic adjustment." Biologia 68.2 (2013): 231-
240.
Jiao, C. J., Jiang, J. L., Ke, L. M., Cheng, W., Li, F. M., Li, Z. X., & Wang, C. Y. (2011). Factors
affecting β-ODAP content in Lathyrus sativus and their possible physiological mechanisms.
Food and Chemical Toxicology, 49(3), 543-549.
Kumar, S., Bejiga, G., Ahmed, S., Nakkoul, H., & Sarker, A. (2011). Genetic improvement of
grass pea for low neurotoxin (β-ODAP) content. Food and Chemical Toxicology, 49(3), 589-
600.
Li, B., & Dewey, C. N. (2011). RSEM: accurate transcript quantification from RNA-Seq data
with or without a reference genome. BMC bioinformatics, 12(1), 323.
Li, B., Fillmore, N., Bai, Y., Collins, M., Thomson, J. A., Stewart, R., & Dewey, C. N. (2014).
Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome Biology,
15(12), 553.
Long, Y.C., YE, Y.H., & Xing, Q.Y. (1996). Studies on the neuroexcitotoxin β‐N‐oxalo‐L‐α, β‐
diaminopropionic acid and its isomer α‐N‐oxalo‐L‐α, βdiaminopropionic acid from the root
of Panax species. International journal of peptide and protein research, 47(1‐2), 42-46.

9
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008). RNA-seq: an
assessment of technical reproducibility and comparison with gene expression arrays.
Genome research, 18(9), 1509-1517.
Oudhia, P. (1999). Allelopathic effects of some obnoxious weeds on germination and
seedling vigour of Lathyrus sativus. FABIS Newsletter 42:32-34
Pan, B., Ren, Y., Gao, J., & Gao, H. (2015). De novo RNA-Seq Analysis of the Venus Clam,
Cyclina sinensis, and the Identification of Immune-Related Genes. PloS one, 10(4).
Pañeda, C., Villar, A. V., Alonso, A., Goñi, F. M., Varela, F., Brodbeck, U., ... & Jones, D. R.
(2001). Purification and characterization of insulin-mimetic inositol phosphoglycan-like
molecules from grass pea (Lathyrus sativus) seeds. Molecular Medicine, 7(7), 454.
Patto, M. V., Skiba, B., Pang, E. C. K., Ochatt, S. J., Lambein, F., & Rubiales, D. (2006).
Lathyrus improvement for resistance against biotic and abiotic stresses: from classical
breeding to marker assisted selection. Euphytica, 147(1-2), 133-147.
Skiba, B., Ford, R., & Pang, E. C. (2005). Construction of a cDNA library of Lathyrus sativus
inoculated with Mycosphaerella pinodes and the expression of potential defence-related
expressed sequence tags (ESTs). Physiological and molecular plant pathology, 66(1), 55-67.
Spencer, P., Ludolph, A., Dwivedi, M. P., Roy, D., Hugon, J., & Schaumburg, H. H. (1986).
Lathyrism: evidence for role of the neuroexcitatory aminoacid BOAA. The Lancet,
328(8515), 1066-1067.
Wang, Z., Fang, B., Chen, J., Zhang, X., Luo, Z., Huang, L., ... & Li, Y. (2010). De novo
assembly and characterization of root transcriptome using Illumina paired-end sequencing
and development of cSSR markers in sweetpotato (Ipomoea batatas). BMC genomics,
11(1), 726.
Xing, G., Zhou, G., Li, Z., & Cui, K. (2000). Accumulation of ABA and ODAP in Lathyrus
sativus under water stress. The journal of applied ecology, 11(5), 693-698.
Yan, Z. Y., Spencer, P. S., Li, Z. X., Liang, Y. M., Wang, Y. F., Wang, C. Y., & Li, F. M. (2006).
Lathyrus sativus (grass pea) and its neurotoxin ODAP. Phytochemistry, 67(2), 107-121.
Yang, T., Jiang, J., Burlyaeva, M., Hu, J., Coyne, C. J., Kumar, S., ... & Zong, X. (2014). Large-
scale microsatellite development in grasspea (Lathyrus sativus L.), an orphan legume of the
arid areas. BMC plant biology, 14(1), 65.
Yang, Z. B., Geng, X., He, C., Zhang, F., Wang, R., Horst, W. J., & Ding, Z. (2014). TAA1-
Regulated Local Auxin Biosynthesis in the Root-Apex Transition Zone Mediates the
Aluminum-Induced Inhibition of Root Growth in Arabidopsis. The Plant Cell Online, 26(7),
2889-2904.
Yaozu, C., Zhixiao, L., Fuhai, L., Xingguo, B., Shenzhan, L., Xuchuan, L., ... & Yaru, L. (1992).
Studies on the Screening of Low Toxic Species of Lathyrus, and Toxicology. Analysis of
Toxins

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