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2. Gene expression profile
A large scale in vivo high throughput screen was performed to identify small molecules that
induce lipid accumulation in the model organism Chlamydomonas reinhardtii. Three
compounds were selected for gene expression analysis using next-generation sequencing
technique. Samples were collected after 72h of treatment with 3 biological replicates for
each compound. Our previous work has shown that compound treatment can induce lipid
droplet accumulation by up to 6 fold and does not severely compromise growth. mRNA was
isolated and sequenced on Illumina Hi-seq 2000. The sequencing reads were mapped to the
genome using Tophat2 and assembled as transcriptome using Cufflinks. Out of 14520
successfully assembled genes, 789, 908 and 2079 genes were differentially expressed when
treated with compound 30, 42 and 84, respectively, with expression patterns different
among treatment and control. Pathway analysis revealed significant metabolic shift under
compound treatment. Changes TCA cycle possibly shift central energy metabolism towards
lipid biosynthesis related pathways. Down-regulation of anabolic pathway and up-
regulation of ER protein processing/degradation suggests the majority of TAG is not
synthesized de novo, but via recycling of cellular components. Up-regulation of nitrogen
assimilation suggests that increased nitrogen intake may reverse the suppressive effects of
compound on growth.
BackgroundMicroalgae, a very large and diverse group of photosynthetic organisms, have attracted
global attention as a renewable energy feedstock. Previous studies conclude that nutrient
stresses, especially nitrogen starvation, induce significant lipid accumulation that might be
used for the production of biofuels. However, nitrogen starvation also causes in degradation of
the photosynthetic apparatus, severely limiting the rate of growth. Additionally as a lipid
induction method, nitrogen limitation is impractical in large-scale production due to technical
and financial drawbacks. This led us to develop a high throughput screening (HTS) system,
which we have employed to identify synthetic chemical compounds that increase lipid
production without severely compromising cell growth or photosynthetic capacity.
In recent years, RNA next-generation sequencing (RNA-seq) has been increasingly used in
transcriptome studies for differential gene expression analysis. Compared to traditional
microarray techniques, RNA-seq provides higher accuracy and larger dynamic range of the
transcript levels and does not require premade microarray chips, which are not readily
available for algae. Several well-accepted software tools adopted by the bioinformatics
community for optimized pipelines were used in this study for computationally intensive data
processing.
In this study, we focused on the transcriptional response of green algae Chlamydomonas
reinhardtii on the level of pathway shifts in an attempt to elucidate the mechanisms of action
of these lipid-inducing small molecules.
Results
Transcriptional Response of Chlamydomonas reinhardtii Treated with Lipid-
inducing Small MoleculesBoqiang Tu, Nishikant Wase, Paul N. Black, Concetta C. DiRusso
Department of Biochemistry, University of Nebraska, 1901 Vine Street, Lincoln, NE 68588-0664
Email: [email protected]
Conclusions
References
Future Directions
Abstract4. Transcriptional change in major anabolic pathways
3. Transcriptional change in other selected pathways
1. Growth and lipid accumulation 6. Expression of selected genes in lipid metabolism
Each compound induced lipid accumulation up to 6-fold higher than control
No compound severely compromised growth, whereas nitrogen deprivation did
Out of 14520 successfully assembled genes, 789, 908 and 2079 genes were
differentially expressed when treated with compound 30, 42 and 84, respectively,
Changes TCA cycle possibly shift central energy metabolism towards lipid
biosynthesis related pathways
.Down-regulation of anabolic pathway and up-regulation of ER protein
processing/degradation suggests the majority of TAG is not synthesized de novo,
but via recycling of cellular components
Up-regulation of nitrogen assimilation suggests that increased nitrogen intake
may reverse the suppressive effects of compound on growth
Compound treatment may be useful for identifying components and mechanisms
that regulate lipid synthesis and can be utilized for biofuel production
Study Design
.
Thanks are due to FATTT Lab members who have contributed intellectual and technical
assistance to this project. This work was supported by Nebraska NSF EPSCoR Award:
1004094 and the Nebraska Center for Energy Sciences Research.
Figure 1. Growth and relative lipid content of cells with different treatments for 72h. (A)
Cells in mid-log phase were washed 2× with TAP media. Compounds were added to a final
concentration of 5 µM in the media. For each treatment and control, three biological replicates of
100 mL each in 250 mL flasks were grown with shaking under white light. Aliquots of 25 mL of
cultures were collected at 72h of incubation. Cells were stained with Nile red and fluorescence
signal measured with a BioTek Synergy plate reader. (B) Confocal images of algal cells stained
with Nile Red to visualize lipid bodies. NF: nitrogen-free; NC: negative control.
3. Transcriptional change in TCA cycle
Figure 4. Information
on selected targets for
qPCR analysis.
2-3 genes each from
several pathways related
to lipid metabolism were
selected (see Fig. 1). All
sequences can be found
in RefSeq database of
NCBI except for DGTT.
The primers were
designed using NCBI
Primer-BLAST online
tool and specificities
checked in RefSeq
database within the
taxonomy of C.
reinhardtii. All the
amplification products
have sizes between 80-
150 bp. RACK1, a
housekeeping gene used
in this study had stable
expression levels
among the different
treatments.
Identified metabolic shifts on transcription level indicate that proteomic and
metabolomics analyses are warranted
Confirm the observed alterations in gene expression and their metabolic effects
using qPCR, western blots and enzyme assays
Perform bioinformatic analysis integrated with particle simulation to identify the
direct targets of these compounds and their mechanisms of action
Identify practical approaches to rescue the growth of algae while producing lipid
Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with
RNA-Seq. Bioinformatics, 25(9), 1105-1111.
Trapnell, C., Williams, ... & Pachter, L. (2010). Transcript assembly and quantification by
RNA-Seq reveals unannotated transcripts and isoform switching during cell
differentiation. Nature biotechnology, 28(5), 511-515.
Kanehisa, M., & Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic
acids research, 28(1), 27-30.
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., ... &
Mesirov, J. P. (2005). Gene set enrichment analysis: a knowledge-based approach for
interpreting genome-wide expression profiles. PNAS, 102(43), 15545-15550.
Luo, W., & Brouwer, C. (2013). Pathview: an R/Bioconductor package for pathway-based data
integration and visualization. Bioinformatics, 29(14), 1830-1831.
(C)
30 42 84 NF NC
A
B
A
Figure 2. Differential gene expression under
compound treatment.
(A) Hierarchical clustering of genes and
conditions. Heat map input data is log 2
transformed fold change. One-minus Pearson
correlation and Euclidian distance were used for
rows and columns, respectively.
(B) Venn diagram of numbers of
differentially expressed genes. Criteria for
differential expression: FDR<0.01 and
(log2(fold change)>2 or log2(fold change)<-2),
compared to control (NC).
Figure 5B. (A) Transcriptional change in Calvin cycle. (B) Transcriptional change in
pentose phosphate pathway. log2 fold change of FPKM of each gene was mapped to the
pathway using KEGG orthology information. Colored areas from left to right represent: 30, 42
and 84. Figure 6. Differential
expression of several genes
related to TAG biosynthesis.
C
72h
Re
lati
ve
Lip
id C
on
ten
t p
er C
ell
0h
24h
48h
72h
0
2
4
6
8
3 0
4 2
8 4
N C
OD
60
0
0h
24h
48h
72h
0 .0
0 .2
0 .4
0 .6
0 .8
A
B
5 µM compound treatment in liquid
culture for 72h
RNA isolation and library prep (Truseq
V3)
Ilumina Hi-seq 2000 sequencing (25
million 100bp single reads / sample)
Mapping reads to genome with
Tophat2 / Bowtie2
Transcriptome assembly with cufflinks from mapped reads
Differential expression analysis
with cuffdiff
Gene Set Enrichment Analysis
Pathway annotation and other statistical
analyses
PLSB
LPA PA DAG
DGTT
G3P TAG
PC lyso-PCPDAT
D G T T 1
T re a tm e n t
FP
KM
NC 3
042
84
0
2
4
6
8
1 0
D G T T 4
T re a tm e n t
FP
KM
NC 3
042
84
0
1
2
3
4
5
P D A T 1
T re a tm e n t
FP
KM
NC 3
042
84
0
2 0 0
4 0 0
6 0 0
8 0 0
P L S B 1
T re a tm e n t
FP
KM
NC 3
042
84
0
5 0
1 0 0
1 5 0
A C L
T re a tm e n t
FP
KM
NC 3
042
84
0
5 0
1 0 0
1 5 0
D G T T 1
Fo
ld c
ha
ng
e
NC
p
NC
s30p
30s
42p
42s
84p
84s
0
5 0
1 0 0
1 5 0
2 0 0
A C L
Fo
ld c
ha
ng
e
NC
p
NC
s30p
30s
42p
42s
84p
84s
0
5
1 0
1 5
A B
A
B
A
B
Figure 2. Transcriptional change in TCA cycle. log2 fold change of FPKM
of each gene was mapped to the pathway using KEGG orthology information.
Colored areas from left to right represent: 30, 42 and 84.
- 0 +
- 0 +
C
(A) TAG biosynthesis pathway:
PLSB: glycerol-3-phosphate
acyltransferase, DGTT:
diacylglycerol acyltransferase,
PDAT: phospholipid:diacylglycerol
acyltransferase. (B) Expression of
individual genes. FPKM:
fragments per kilobase of transcript
per million mapped reads. (C)
comparison between qPCR and
RNA-seq results (indicated by “p”
and “s”, respectively)
A
B
C
- 0 +
- 0 +
Figure 6. (A) Transcriptional
change in N-glycan biosynthesis
pathway. (B) Transcriptional
change in protein processing in ER.
(C) Transcriptional change in
molybdenum cofactor biosynthesis
pathway. log2 fold change of FPKM
of each gene was mapped to the
pathway using KEGG orthology
information. Colored areas from left
to right represent: 30, 42 and 84.