Annie KrichtenBio 240 Section 903

TA: JJ Hu

MicroRNA and gene expression in Arabidopsis thaliana in low phosphate and sulfur deficient growing conditions

Introduction

With global warming causing climate change and other factors such as land use changing,

people may need to get more out of the land to support the growing human population.

Therefore it becomes essential to look into what growing conditions or gene expression may lead

to optimum crop or plant yield. MicroRNA study is now very interesting because miRNAs play

an important role in plant growth and development (Axtell, Burpee, Nelson, 2012).

Arabidopsis thaliana is a model plant organism for research dealing with miRNA control

and behavior in response to different environmental conditions (Axtell, Burpee, Nelson, 2012).

Arabidopsis is used because of its short life cycle, ease in growing it under laboratory conditions,

and because the miRNAs found in Arabidopsis are similar to those found in wheat and other

grains, which constitute a large staple in human diet (Chiou, 2007; Chuck et al, 2011; Poethig,

2009).

Plants need certain nutrients for growth and development, but with certain soils lacking

those needed nutrients and it becoming harder to supply and manufacture the same quality or

quantity of fertilizers, some areas lack the needed nutrients. Thus, not only must microRNA

function be studied to understand the mechanisms of how they function in Aridopsis growth, but

the microRNAs must also be studied in stressful conditions, such as brought on by drought or

heavy rain, temperature extremes, or a lack of nutrients. Factors to be monitored in growth

include nutrient uptake and flowering of the plants. The outcomes found with respect to stress

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and microRNA expression may help scientists (fulfill the purpose of this study and) understand

better which factors to manipulate in order to yield plants that better fit the needs of whatever

process they are to be used in, like biofuel production, crop growth for agriculture, etc.

In the following experiments, nutrient deficiency stimulated responses that were observed

by looking at plant growth and understanding the role of micrRNA activity in the suppression

and expression of certain genes. Arabidopsis seeds were plated in control medium and in

medium with no sulfur or low phosphorous levels, plant tissue was extracted, and RNA was

gathered from the tissue, so that levels of the microRNAs could be determined. Four miRNAs

(156, 395, 398, 399) were tested with a control (a U6 spliceosomal RNA).

While using these microRNAs, it is important to understand their biological functions.

MicroRNAs attach to target genes and may either bind to the target and prevent its translation, or

the microRNA sequence may be cut. The function of miR156 is to express the juvenile phase of

plant growth and to regulate when a plant transitions from the juvenile phase to the adult phase.

In Arabidopsis, the target genes of miR156 are Squamosa Promoter binding proteins of certain

transcription factors (Wu, Gang; Park, Mee Yeon; Conway, Susan R.; Wang, Jia-Wei; Weigel,

Detlef; Poethig, R. Scott; 2009). The function of miR395 was found during a research study

exploring the sensing of nutrient stress, and was determined to help in the absorption and

distribution of sulfur throughout the plant (Chiou, 2007). The function of miR398 is to regulate

the expression levels of other genes using different mechanisms (Bouché, Nicolas; 2010). The

function of miR399 is to regulate phosphate homeostasi and its target gene is Phosphate 2 (Kuo,

Hui-Fen; Chiou, Tzyy-Jen; 2011). The U6 spliceosomal RNA has the function of combining

with other mRNAs to form a spliceosome and one study even found that it may target a HIV-1

RNA structure (Butcher, S. E.; 2010).

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Goals of the experiment were to become familiar with Arabidopsis thaliana, understand

miRNAs and how they control gene expression, understand how plants respond to nutrient or

environmental stress, and to determine how certain methods of miRNA purification, reverse

transcription, and qRT-PCR procedures could lead to better results in study (Axtell, Burpee,

Nelson, 2012). It was hypothesized that in extreme conditions, miRNA becomes more active to

suppress gene expression for growth or flowering during nutrient deficiencies or drought, and

that seed production may be allowed to continue in heavy rainfall. More specific to this

experiment, however, it was hypothesized that miRNAs would behave to suppress flowering and

increase root structure in an attempt to have a greater success in nutrient uptake.

Materials and Methods

In order to conduct this experiment, Arabidopsis thaliana seeds were plated, cultured,

and used to extract and amplify genetic material from the plants. Medium types for growth

included full medium, no sulfur, or low phosphate. 30-100 seeds were sprinkled onto the

medium in each plate, and the plate was sealed with microspore surgical tape. Pictures of the

plates were taken to monitor growth across a timeframe of several weeks. During extraction

process, 25-35 plants were taken from the plates and transferred to a 1.5 mL microcentrifuge

tube. Lysis mix, tissue homogenization, centrifuging, filtration, and multiple sets of washing

turned the seedling tissue into a lysate supertanant and then an elution solution so that

purified RNA would be available for further study. Next, pcr analysis was divided across the

class such that all types of medium’s plant DNA would be tested for controls and the four

miRNAs. Reverse Transcriptase was used to convert the miRNA to cDNA for QRT-PCR

plating of a 96-well plate. The components of the U6 and miRNA master mixes for pcr

testing are as depicted below:

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Master Mix for U6 (U6MM)Component Per reaction x3.5 water 9l 31.5l2X SYBR Green master mix (Qiagen) 10l 35lU6 forward oligo (@ 10M) 1l 3.5lU6 reverse oligo (@10M) 1l 3.5l

Master mix for microRNA (miRNA MM)Component Per reaction x3.5 water 9l 31.5l2X SYBR Green master mix (Qiagen) 10l 35lmiRNA-specific forward oligo (@ 10M) 1l 3.5lUniversal SL-reverse oligo(@10M) 1l 3.5l

21 l of the appropriate master mix were dispensed into the assigned wells with the cDNA. PCR

is used to amplify a targeted DNA molecule, and qPCR is a quantitative real time polymerase

chain reaction to monitor real-time amplification of the genetic material via measuring the

intensity of fluorescence.

Once the qRT-PCR process was completed, the data were analyzed. Analysis was

performed by comparing “no sulfur” and low phosphate conditions to the full medium conditions

using the four different miRNAs. This analysis was done via excel and certain equations. To find

the efficiency for each primer set, the equation, E=10(−1 /S), was used. The equation,

∆ C t=Ctcontrol−C t

experimental, was used to find the change in miRNA accumulation. To find relative

accumulation values, the equation, RA=E∆ C t, was used. Also, the equation

RAn=(E target)

∆C t target

(Ereference)∆C t reference could be simplified to RAn=

RA target

RA reference, and was used to find normal

relative accumulation. Lastly, the medians of the RAns for each miRNA were calculated and

made into a chart to analyze the data more easily. All of the experimental procedures can be

found in the lab manual (Axtell, Burpee, and Nelson, 2012).

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Results

My group cultured a plate with low phosphate deficiency and plated low phosphate and

full medium genetic material with miRNA 399 and U6 for the pcr tests. When looking at the

data, some numbers had to be changed from original qPCR data to appropriate numbers due to

technical errors. Our negatives all reported as “undetermined,” our full medium data was normal,

and half of the low phosphorous data needed to be replaced with more correct values. At the

different stages of the Arabidopsis life cycle pictures were taken:

Figure 1: Pictures of Growth over Time

Full Medium Low Phosphorous No Sulfur

Week 0

Jan 15th, 2013

*No picture provided *No picture provided

5

Jan

18th,

2013

Jan

25th,

2013

Feb

7th,

2013

*Some pictures taken from other sections.

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According to these growth pictures, certain observations may be made, which may later

be tied to certain miRNA activity levels in each of the plants. For instance, growth appears

similar until Jan 25th, by which time the plants growing in the full medium already look greener

and fuller, and flowering (leaf) success appears to diminish from the full medium plate to the no

sulfur plate to the low phosphorous plate. In the last week observed, the low phosphorous plants

still appear the most troubled, with more roots, fewer leaves, and more black coloration as

opposed to bright green. No sulfur plants have smaller leaves, with some discoloration, and more

clustered growth than in the low phosphorous plating scenario.

Besides observing the plants grow, data were also collected from the miRNA analysis.

An example of the qPCR results are as shown in the figure below depicting how florescence is

recorded while the number of cycles, or time, passes during the pcr amplification process.

Figure 2: qPCR Florescence Over Time

In addition, class and sample data may be analyzed with the formulas and equations

mentioned in the materials and methods section in order to get a better representation of how

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miRNA abundance differed in the plants as nutrient stress varied in comparison to the full

medium data. Delta Cts, RAs (Relative Abundances), RAns (normalized RAs), and median

RAns were calculated for class and section data and the results are described below.

Table 1: Median Relative Abundances (Normalized)

Median RAn's

Section Data Class Data Sample Data

Low P No S Low P No S Low P No S

miR1567.86997739

20.52022001

213.664025 28.393489 1.862930111 1.22768322

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miR3950.32150942

65742865.39

74.1189902 3143889.3 0.046397251 1158.41385

8

miR39857.4563369

514129.5227

756.242062 2023.5179 0.034190325 0.15053789

3

miR39934.1239914

2 0.59602639187509.78 53933.866 20.76552394 1.19811125

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Compared to the sample data (used at a “control”), the section data is pretty different, and

the class data is very different.

Figure 3

miR156 miR395 miR398 miR3990.1

110

1001000

10000100000

100000010000000

Relative Abundance Variance of miRNAs for Different Nutrient Conditions in Sec-

tion Data

LowPNo S

miRNA types

Med

ian

Rela

tive

Abun

danc

e(s

cale

of l

og 1

0)

*This is the chart showing change in miRNA accumulation for section 903 data.

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In the section data, miR156 and miR399 have higher abundances of miRNA in the low

phosphorous scenarios. In no sulfur conditions, miR395 and 398 had much higher abundances.

Abundance was increased in all cases except for no sulfur miR156 and miR399, and low

phosphorous miR395. Abundance of miRNAs was most greatly increased in the no sulfur cases

with miR395 and miR398.

Figure 4

miR156 miR395 miR398 miR3991

10

100

1000

10000

100000

1000000

10000000

Change in miRNA Relative Abundances in Varying Nutrient Conditions in Class

Data

LowPNo S

miRNA types

Med

ian

Rela

tive

Abun

danc

e(s

cale

of l

og 1

0)

*This is the chart for change in miRNA accumulation for class data.

This data differs from the section data more, with all miRNA abundances being increased

for each nutrient condition. MiR156 and mi399 abundances are closer (according to the log

scale) for the two nutrient conditions. In all cases but the miR399, the no sulfur condition

promoted miRNA abundance more.

Discussion

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In the section data, miRNA156 is up regulated for low phosphate and down regulated for

no sulfur, miR395 is up regulated for no sulfur and down regulated for low phosphorous,

miR398 is up regulated for low phosphate and up regulated for no sulfur, miR399 is up regulated

for low phosphorous and down regulated for no sulfur. In the class data, all miRNAs are up

regulated for no sulfur and up regulated for low phosphate.

In looking at the plant growth and miRNA abundances, at least for the class data, my

hypothesis was correct that roots would increase and flowering would decrease along with an

increase in miRNA activity. In exploring why this occurred, I would assume the root branching

increased in an attempt to find and absorb more nutrients, and flowering decreased to instead use

energy for nutrient and growth processes. Reasoning for why the miRNAs might have behaved

the way they did can be related to their functions as described in the background section. For

miR156, the microRNA helps control the transition to adult plants. Perhaps the lack of mature,

successfully flowering plants is due to an up regulation of the miR156. In addition, miR395 was

allowed to exist at high abundances in the plant in no sulfur conditions because the miRNA

functions in sulfur uptake and distribution throughout the plant. The plant could have increased

its miR395 levels either in an attempt to absorb any sulfur even though none was present (id it

really helps in uptake), or because the microRNA would suppress gene expression to work in the

uptake of the nutrient (thus stopping the futile searching process and allowing the plant to divert

energy elsewhere – Perhaps why the no sulfur plant looks a little more productive than the low

phosphorous plant). MiR398 deals with copper and zinc, and so its activity was not restricted

too much. In conditions with low phosphorous, miR399 was up regulated, possibly because it

was trying to help the plant return to normal phosphate balance.

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The sample data, class data, and section data are inconsistent with each other, suggesting

that errors skewed data due to human error in procedure, or pcr problems, or error in data

analysis. In the sample data, it appears that LS_3 might have been contaminated (errors every

time) and other samples may have had errors just from the aforementioned or following errors.

Some human errors may include technical errors like contamination or miss loading. I would

suggest finding better ways to prevent contamination like conducting the experiment in a sterile

area or finding better methods for tissue homogenization, as some of our plant fluid mixtures

splashed out of the microcentrifuge tube (may be why half of the low phosphorous data from us

had to be exchanged with more correct values). In addition, perhaps a longer growth period

would have been better, to provide more tissue of adequate quality from the plants growing in

the poorer nutrient conditions.

Future studies could deal with working to understand what microRNAs lead to plants

with more branching and stalkiness to be best for biofuel, or plants with larger fruits or seeds for

food production. In addition, more experiments could be done with monitoring changes if using

other types of microRNAs, other plants, or different environmental stressors. This experiment is

very relevant and important to understand in the larger scheme, because finding answers to the

questions addressed in this lab may help solve problems relating to agriculture in places that can

no longer support growth in the ways needed. Data from this experiment may also help to

explain how plants adapt to different environments, and how microRNA manipulation may allow

a plant to grow to better quality despite poor nutrient conditions.

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References

1. Axtell, M., Burpee, D. and Nelson, K. (2012). microRNA and Plant Nutrition.

2. Bouché, Nicolas. (2010). New insights into miR398 functions in Arabidopsis. Plant

signaling and behavior. 5:684-686.

3. Butcher, S. E. (2010). STRUCTURAL STUDIES OF U6 SNRNA. University of

Wisconsin.

4. Chiou, T-J. 2007. The role of microRNAs in sensing nutrient stress. Plant, Cell and

Environ. 30:323-332.

5. Chuck, G.S., Tobias, C., Sun, L., Kraemer, F., Li, C. et al. 2012. Overexpression of the

maize Corngrass1 microRNA prevents flowering, improves digestibility, and increases

starch content of switchgrass. PNAS 108: 17550-17555.

6. Kuo, Hui-Fen; Chiou, Tzyy-Jen. (2011). The Role of MicroRNAs in Phosphorus

Deficiency Signaling. Agricultural Biotechnology Research Center.

7. Nischal L, Mohsin M, Khan I, Kardam H, Wadhwa A, Abrol YP, Iqbal M, Ahmad A. :

Identification and Comparative Analysis of MicroRNAs Associated with Low-N

Tolerance in Rice Genotypes. PLoS One (A Peer Reviewed, Open Access Journal).

December 5, 2012

8. Poethig, R.S. 2009. Small RNAs and developmental timing in plants. Curr Opin Genet

Dev. 19: 374-378.

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9. Wu, Gang; Park, Mee Yeon; Conway, Susan R.; Wang, Jia-Wei; Weigel, Detlef; Poethig,

R. Scott. (2009). The sequential action of miR156 and miR172 regulates developmental

timing in Arabidopsis. NIHPA Manuscripts. 138:750-759.

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