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: btu2@unl.edu

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.