Biofuel Optim

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ALMA MATER STUDIORUM

UNIVERSIT DI BOLOGNA

FACOLT DI SCIENZE MATEMATICHE, FISICHE E NATURALICORSO DI LAUREA MAGISTRALE IN BIOINFORMATICA

Optimising Biofuel productioncomputational characterisation of gene and

related promoter and enhancer involved in

fatty acid production in algae

Candidato: Relatore:

Id Antonino Prof. Giovanni Perini

ANNO ACCADEMICO 2007/2008

SESSIONE III

Supervisione:

Prof. Ugur Sezerman


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per I miei genitori


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Abstract

Photosynthetic organisms, including plants, algae, and some photosynthetic

bacteria, efficiently utilize the energy from the sun to convert water and CO2

from the air into biomass . Basic research efforts have been made to produce

renewable fuels and chemicals from biomass. Particularly, algae (microscopic,photosynthetic organisms that live in saline or freshwater environments) were

investigated for their ability to produce lipids as a feedstock and primary storage

molecules for liquid fuel or chemical production. In this respect, the research

conducted on algae emphasized the use of photosynthetic organisms from

aquatic environments, especially species that grow in environments unsuitable

for crop production.

Fatty acids, the building blocks for triacylglycerols (TAG) and all other cellular

lipids are synthesized in the chloroplast using a single set of enzymes, of which

acetyl CoA carboxylase (ACCase) is the key in regulating fatty acid synthesis

rates.

However, the expression of genes involved in fatty acid synthesis are currently


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relatively unknown with reference to algae. Synthesis and sequestration of TAG

into cytosolic lipid bodies appears to be a protective mechanism by which algal

cells cope with stress conditions little is known about genomic sequence andregulation of TAG formation at the molecular and cellular level.

In this project we designed primers to use the predicted gene from the strain

already sequenced to recognize and characterize the (ACCase) from

Scenedesmus Protuberans, a strain of algae that were previously screened as a

potential feedstock of fatty acids taken from strains investigated. We also used

the flanking region of the homologous predicted gene to run up two

computational tools to seek for the related conserved regulatory motif,

Transcription Factor Binding Site (TFBS) and identify the differences amongst

the sequences according to the differing amount of fatty acids in these strain.


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Index

1 Introduction ..........................................................................................................................1

1.1Statement of the problem ..................................................................................................1

2 Background information .....................................................................................................5

2.1 Algae ............................................................................................................................5

2.2 Fatty acid composition...................................................................................................9

2.3 Biosynthesis of Fatty acids and triaciyglycerols ............................................................14

2.4 Regulation of fatty acid Synthesis .................................................................................18

2.5 Knowledge in other species............................................................................................19

2.6 Feedback regulation .......................................................................................................23

2.7What Controls Promoter Activity of FAS Genes?...........................................................24

3 State of the art ...................................................................................................................25

3.1 Comparison of lipid metabolism in algae and higher plants..........................................25

3.2 Factor affecting tryacilglycerolipids accumulation and fatty acids composition............26

3.2.1 Nutrients..................................................................................................................27

3.2.2Temperature.............................................................................................................28

3.2.3Light intensity..........................................................................................................29

3.2.4Growth phase and Physiological status ...................................................................30

3.2.5Physiological roles of triacylglycerol accumulation ...............................................32

3.3Algae genomic and proposed model system in biofuel production ................................33

3.4Acetil CoA carboxylase protein and genetic characterization ........................................38

4 Methods................................................................................................................................40

4.1 Genetic source ...............................................................................................................40

4.2 Sequence analysis...........................................................................................................43

4.3 Computational methodologies........................................................................................44

4.3.1Bioprospector ..........................................................................................................46

4.3.1.1Scoring segment with background Markov dependency..................................47

4.3.1.2Using motif score distribution to measure goodness of a motif.......................48

4.3.2GALF_P ..................................................................................................................48

4.3.2.1Representation .................................................................................................494.3.2.2Fitness Evaluation............................................................................................50

4.3.2.3Selection and Genetic Operators .....................................................................51

4.3.2.4Local filtering Operator ..................................................................................52

4.3.2.5Replacement Strategy.......................................................................................53

4.3.2.6Shift Operator ..................................................................................................53

4.3.3Implementation........................................................................................................54

4.4 Primer design ................................................................................................................55

4.5Experimental procedure .................................................................................................58

5 Result and future perspective.............................................................................................65


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1 Introduction

1.1 Statement of the problem

The current global energetic crisis, and the possibility of an ever decreasing oil

and gasoline production, leads to the necessity to research and explore effective

alternatives. Among these effective alternatives biodiesel stands out. Biodiesel

can be defined as the ester derived from oils and fats extracted from renewable

biological sources (1)

Produced by the trans-esterification of triaglycerides with methanol, and

consisting of long chain made of alkyl, methyl propyl or ethyl esters, Biodiesel

comes as an alternative to petroleum-based diesel fuel (2) . It can be used

alone, or mixed with conventional petro-diesel in unmodified diesel-engine

vehicles. Biodiesel is cleaner than petroleum diesel, and distinguished from the

straight vegetable oils (SVO), sometime classified by specialists as "waste


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vegetable oil", "WVO", "used vegetable oil", "UVO", "pure plant oil", "PPO"(3).

It is virtually free of sulphur, therefore reducing the quantity of sulfur oxides

normally produced during combustion. The emission of hydrocarbons, carbon

monoxide and particulates during combustion are also significantly reduced in

comparison to emission from petroleum diesel. These properties make biodiesel

effective and in full compliance with the Kyoto Protocol concerning the

greenhouse gas emission. An additional benefit of biodiesel is that it is thought

to be a non-toxic, biodegradable fuel and provides essentially the same energy

content and power output as petroleum-based diesel fuel while reducing

emissions.

Biodiesel production is constantly increasing, with an average annual growth

rate of over 40% during the 2002-2006 period (4). In 2006, the amount of

biodiesel produced in the world ranged 5-6 million tonnes, with 4.9 million

tonnes processed in Europe (of which 2.7 million tonnes in Germany),and great

part of the remaining quantity processed in the USA (5). the sole European

fabrication increased to 5.7 million tonnes (6).Accordingly, the volume of

biodiesel produced in Europe in 2008 has been calculated for a total of 16

million tonnes. These figures have to be considered as part of the circa 490

million tonnes (147 billion gallons) demand for diesel fuel in the US and

Europe(6) . Moreover, the global production of vegetable oil for all purposes in

2005/06 touched 110 million tonnes, of which about 34 million tonnes of palm
http://en.wikipedia.org/wiki/Biodiesel#cite_note-31http://en.wikipedia.org/wiki/Biodiesel#cite_note-33http://en.wikipedia.org/wiki/Biodiesel#cite_note-33http://en.wikipedia.org/wiki/Biodiesel#cite_note-33http://en.wikipedia.org/wiki/Biodiesel#cite_note-31


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oil and soybean oil(7) .

However, feedstocks limited efficiency per acre is still the main obstacle to

consider bio-fuel as a reliable supply to sustain the current global fuel demand,

and the increasing oil demand along with the high costs of good quality

vegetable constantly challenge bio-fuels reliability on industrial scale. Some

typical yields in cubic decimeters (liters) of biodiesel per hectare (10,000 square

meters):

Algae: 2763 dm3 (liter) or more (~300 gallons per acre; est.- see soy

figures and DOE quote below)

Hemp: 1535 dm

3 (8)

Chinese tallow: 772 dm3(9) - 970 GPa(10)

Palm oil: 780 - 1490 dm3(11)

Coconut: 353 dm3 (11)

Rapeseed: 157 dm3] (11)

Soy: 76-161 dm3 in Indiana (12)(Soy is used in 80% of USA biodiesel(13))

Peanut: 138 dm3 (11)
http://en.wikipedia.org/wiki/Biodiesel#cite_note-46http://en.wikipedia.org/wiki/Biodiesel#cite_note-47http://en.wikipedia.org/wiki/Biodiesel#cite_note-gristmill-48http://en.wikipedia.org/wiki/Biodiesel#cite_note-gristmill-48http://en.wikipedia.org/wiki/Biodiesel#cite_note-Soy-improving_yield-50http://en.wikipedia.org/wiki/Biodiesel#cite_note-46http://en.wikipedia.org/wiki/Biodiesel#cite_note-47http://en.wikipedia.org/wiki/Biodiesel#cite_note-gristmill-48http://en.wikipedia.org/wiki/Biodiesel#cite_note-gristmill-48http://en.wikipedia.org/wiki/Biodiesel#cite_note-Soy-improving_yield-50


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Sunflower: 126 dm3 (11)

Food-grade vegetable oil pricing is on a similar upward ramp as food in general.

Non-food grade vegetable oils, however, are also used to make biodiesel. In

some poor countries the rising price of vegetable oil is causing problems(14)

(15). Some scientists propose that fuel can be made from non-edible vegetable

oils like camelina, jatropha or seashore mallow which can thrive on marginal

agricultural land where many trees and crops will not grow, or render scarce

harvests.

Others trace back this problem to different sources. Some farmers may give up

producing food crops for biofuel crops for economical reasons, even if the new

crops are not edible. Of course, the increasing demand for first generation

biofuel is likely to result in price increases for many kinds of food supplies. On

the other hand, some have pointed out that such situation might bring a wave of

financial gain to those poor farmers and poor countries investing in bio-fuel

crops(16).


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2 Background information

2.1

Algae represent an extremely diverse, yet highly specialized group of organism

that live in diverse ecological habitats such as freshwater, brackish, marine and

hyper-saline, with a range of temperature and pH, and unique nutrient

availabilities.(17)

With over 40 000 species already identified, algae are classified in multiple

major groupings as follows :

cyanobacteria (Cyanophyceae)

green algae (Chlorophyceae)

diatoms (Bacillariophyceae)

yellow-green algae (Xanthophyceae)

golden algae (Chrysophyceae)

red algae (Rhodophyceae)


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brown algae (Phaeophyceae)

dinoflagellates (Dinophyceae)

pico-plankton (Prasinophyceae and Eustigmatophyceae)

Several additional division and classes of unicellular algae have been described,

and details of their structure and biology are available. (18)

The ability of to survive or proliferate over a wide range of environmental

condition is, to a large extent, reflected in the tremendous diversity and

sometime unusual pattern of cellular lipids as well as the ability to modify lipid

metabolism efficiently in response to changes in environmental condition.(19)

(20)(21)

The lipids may include, but are not limited to, neutral lipids, polar lipids, wax

ester, sterols and hydrocarbons, as well as prenyl derivatives such as tocopherols,

carotetenoids ,terpenes, quinones and phytylated pyrrole derivatives such as the

clorophylls. Unlike higher plants where individual classes of lipid may be

synthesized and localized in a specific cell, tissue or organ, many of these

different types of lipids occur in a single algal cell. After being synthesized,

TAGs are deposited in densely packed lipid bodies located in cytoplasm of the

algal cell, although formation and accumulation on of lipid bodies also occur in

the inter-thylakoid space of the chloroplast in certain green algae, such as


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Dunaliella bardawill.(22) in the latter case, the chloroplastic lipid bodies are

referred to as plastoglobuli. Hydrocarbons are another type of neutral lipid

that can be found in algae at quantities generally


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suitable for the conventional agriculture

utilize growth nutrients such as nitrogen and phosphores from a variety waste-

water sources (e.g. agricultural run-off, concentrate animal feed operations, and

industrial and municipal waste-water) providing additional benefit of waste-

water bio-remediation ,

sequester carbon dioxide from flue gasses emitted from fossil fuel-fired power

plants and other sources, thereby reducing emissions of a major greenhouse gas

produced value added co-products or by-products (e.g. byopolimers, protein,

polysaccharides, pigments, animal feed, fertilizer and H2)

grow in suitable culture vessels (photo-bioreactors) throughout the year with an

annual biomass productivity, on an area basis, exceeding that of terrestrial plants

by approximately tenfold

Based upon the photosynthetic efficiency and growth potential of algae,

theoretical calculation indicate that annual oil production of 30 000 l or about

200 barrels of algal oil per hectare of land may be achievable in mass culture of

oleaginous algae, witch is 100-fold greater than that of soybeans , a major

feedstock currently being used for biodiesel in the USA.

While the 'algae for fuel' concept has been explored in the USA and some other


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countries, with interest and funding growing and waning according to the

fluctuations of the world petroleum oil market over the past few decades, no

effort in algae based biofuel production have proceeded beyond rather small

laboratory or field testing stage. The lipid yields obtained from algal mass

culture effort performed to date fall short of the theoretical maximum (at least

10-20 times lower), and have historically made algal oil production technology

prohibitively expensive.(27)(28)

Recent soaring oil prices, diminishing world oil reserves, and the environmental

deterioration associated with fossil fuel consumption have generated renewed

interest in using algae as an alternative and renewable feedstock for fuel

production. However, before this concept can become commercial reality, many

fundamental biological questions relating to the biosynthesis and regulation of

fatty acid and TAG in algae need to be fully answered. Clearly, physiological and

genetic manipulations of growth and lipid metabolism must be readily

implementable, and critical engineering breakthroughs related to alga mass

culture and down-stream processing are necessary.

2.2 Fatty acid composition


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Algae synthesize fatty acids as building blocks for the formation of various types

of lipids. The most commonly synthesized fatty acids have chain lengths that

range from C16 to C18 (Table1), similar to those of higher plants (29). Fatty

acids are either saturated or unsaturated, and unsaturated fatty acids may vary in

the number and position of double bonds on the carbon chain backbone. In

general, saturated and mono-unsaturated fatty acids are predominant in most

algae examined (26). Specifically, the major fatty acids are C16:0 and C16:1 in

theBacillariophyceae, C16:0 and C18:1 in the Chlorophyceae, C16:0 and C18:1

in the Euglenophyceae, C16:0, C16:1 and C18:1 in the Chrysophyceae, C16:0

and C20:1 in the Cryptophyceae, C16:0 and C18:1 in the Eustigmatophyceae,

C16:0 and C18:1 in the Prasinophyceae, C16:0 in the Dinophyceae, C16:0,

C16:1 and C18:1 in the Prymnesiophyceae, C16:0 in the Rhodophyceae, C14:0,

C16:0 and C16:1 in the Xanthophyceae, and C16:0, C16:1 and C18:1 in

cyanobacteria (30).Polyunsaturated fatty acids (PUFAs) contain two or more

double bonds. Based on the number of double bonds, individual fatty acids are

named dienoic, trienoic, tetraenoic, pentaenoic and hexaenoic fatty acids. Also,

depending on the position of the first double bond from the terminal methyl end

( ) of the carbon chain, a fatty acid may be either an 3 PUFA (i.e. the third

carbon from the end of the fatty acid) or an 6 PUFAs (i.e. the sixth carbon

from the end of the fatty acid). The major PUFAs are C20:5 3 and C22:6 3 in

Bacillarilophyceae, C18:2 and C18:3 3 in green algae, C18:2 and C18:3 3 in


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Euglenophyceae, C20:5, C22:5 and C22:6 in Chrysophyceae, C18:3 3, 18:4 and

C20:5 in Cryptophyceae, C20:3 and C20:4 3 in Eustigmatophyceae, C18: 3 3

and C20:5 in Prasinophyceae, C18:5 3 and C22:6 3 in Dinophyceae, C18:2,

C18:3 3 and C22:6 3 in Prymnesiophyceae, C18:2 and C20:5 in

Rhodophyceae, C16:3 and C20:5 in Xanthophyceae, and C16:0, C18:2 and

C18:3 3 in cyanobacteria (30)(31)

In contrast to higher plants, greater variation in fatty acid composition is found

in algal taxa. Some algae and cyanobacteria possess the ability to synthesize

medium-chain fatty acids (e.g. C10, C12 and C14) as predominant species,

whereas others produce very-long-chain fatty acids (>C20). For instance, a C10

fatty acid comprising 2750% of the total fatty acids was found in the

filamentous cyanobacterium Trichodesmium erythraeum (32), and a C14 fatty

acid makes up nearly 70% of the total fatty acids in the golden alga Prymnesium

parvum (23). Another distinguishing feature of some algae is the large amounts

of very-long-chain PUFAs. For example, in the green alga Parietochlorisincise

(33), the diatom Phaeodactylum tricornutum and the dinoflagellate

Crypthecodinium cohnii (34), the very-long-chain fatty acids arachidonic acid

(C20:4 6), eicosapentaenoic acid (C20:5 3) or docosahexaenoic acid

(C22:6 3) are the major fatty acid species. accounting for 33.642.5%,

approximately 30% and 3050% of the total fatty acid content of the three

species, respectively.


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It should be noted that much of the data provided previously comes from the

limited number of species of algae that have been examined to date, and most of

the analyses of fatty acid composition from algae have used total lipid extracts

rather than examining individual lipid classes. Therefore, these data represent

generalities, and deviations should be expected. This may explain why some

fatty acids seem to occur almost exclusively in an individual algal taxon. In

addition, the fatty acid composition of algae can vary both quantitatively and

qualitatively with their physiological status and culture conditions.

The properties of biodiesel are largely determined by the structure of its

component fatty acid esters (35).The most important characteristics include

ignition quality (i.e. cetane number), cold-flow properties and oxidative stability.

While saturation and fatty acid profile do not appear to have much of an impact

on the production of biodiesel by the trans-esterification process, they do affect

the properties of the fuel product. For example, saturated fats produce a

biodiesel with superior oxidative stability and a higher cetane number, but rather

poor low-temperature properties. Biodiesels produced using these saturated fats

are more likely to gel at ambient temperatures. Biodiesel produced from

feedstocks that are high in PUFAs, on the other hand, has good cold-flow

properties. However, these fatty acids are particularly susceptible to oxidation.

Therefore, biodiesel produced from feedstocks enriched with these fatty acid

species tends to have instability problems during prolonged storage.


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Table 1: Abbreviation of algal species: B.a., Biddulphia aurica (Orcuut and patterson,1975); C.sp. Chaetoceros sp. (Renaud et al., 2002); N.sp., Nannochloropsis sp.

(Sukenik,1999); M.s., Monodus subterraneus (Cohen,1999); C.s., Chlorella sorokiniana

(Patterson,1970); C.v., Chlorella vulgaris (Herris et al.,1965); P.i., Parietochloris incise

(Khonizin-Goldberg et al., 2002); E.h., Emiliania huxleyi (Volkman et al., 1981); I.g.,

Isochrysis galbana (Volkman et al., 1981); P.p., Phaeomonas parva (Kawachi et al., 2002);

G.c., Glossomastrix chrysoplasta (Kawachi et al., 2002); A.sp., Aphanocapsa sp. (Kenyon,

1972); S.p., Spirulina platensis (Mhling et al., 2005); T.e., Trichodesmium erythraeum

(Parker et al ., 1967); H.b., Hemiselmis brunescens (Chuecas and Riley, 1969); R.l.,

Rhodomonas lens (Beach et al., 1970); G.s., Gymnodinium sanguineum; S.sp., Scrippsiella

sp. (Mansour et al., 1999).


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2.3 Biosynthesis of Fatty acids and triaciyglycerols

Lipid metabolism, particularly the biosynthetic pathways of fatty acids and TAG,

has been poorly studied in algae in comparison to higher plants. Based upon the

sequence homology and some shared biochemical characteristics of a number of

genes and/or enzymes isolated from algae and higher plants that are involved in

lipid metabolism, it is generally believed that the basic pathway of fatty acids

and TAG biosynthesis in algae are directly analogous to those demonstrated in

higher plants. It should be noted that because the evidence obtained from algal

lipid research is still fragmentary, some broad generalization are made in this

section based on limited experimental data.

In algae, the de novo synthesis of fatty acid occur primarily in the chloroplast. A

generalized scheme for fatty acids biosynthesis is show in (Figure 2). Overall,

the pathway produces a 16- or 18-carbon fatty acids or both. These are then used

as a precursor for the synthesis of chloroplast and other cellular membranes as

well as for the synthesis of neutral storage lipids, manly TAGs. The committed

step in fatty acid synthesis is the conversion of acetyl CoA to malonyl CoA,

catalyzed by acetyl CoA carboxylase (Acetyl-CoA carboxylase). In the


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chloroplast, photosynthesis provides an endogenous source of acetyl CoA, and

more than one pathway may contribute to maintaining the acetyl CoA pool. In

oil seed plants, a major route of carbon flux to fatty acid synthesis may involve

cytosolic glycolysis to phosphoenolpyruvate (PEP), which is then preferentially

transported from the cytosol to the plastid, where it is converted to pyruvate and

consequently to acetyl CoA (36) In green algae, as glycolysis and pyruvate

kinase (PK), which catalyzes the irreversible synthesis of pyruvate from PEP,

occur in the chloroplast in addition to the cytosol (37) it is possible that

glycolysis-derived pyruvate is the major photosynthate to be converted to acetyl

CoA for de novo fatty acid synthesis. An Acetyl-CoA carboxylase is generally

considered to catalyze the first reaction of the fatty acid biosynthetic pathway

the formation of malonyl CoA from acetyl CoA and CO2. This reaction takes

place in two steps and is catalyzed by a single enzyme complex. In the first step,

which is ATP-dependent, CO2

(from HCO3) is transferred by the biotin

carboxylase prosthetic group of Acetyl-CoA carboxylase to a nitrogen of a biotin

prosthetic group attached to the -amino group of a lysine residue. In the second

step, catalyzed by carboxyltransferase, the activated CO2 is transferred from

biotin to acetyl CoA to form malonyl CoA (29). malonyl CoA, the product of the

carboxylation reaction, is the central carbon donor for fatty acid synthesis. The

malonyl group is transferred from CoA to a protein co-factor on the acyl carrier


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protein (ACP; Figure 2)

All subsequent reactions of the pathway involve ACP until the finished products

are ready for transfer to glycerolipids or export from the chloroplast. The

malonyl group of malonyl ACP participates in a series of condensation reactions

with acyl ACP (or acetyl CoA) acceptors. The first condensation reaction forms

a four-carbon product, and is catalyzed by the condensing enzyme, 3-ketoacyl

ACP synthase III (KAS III). Another condensing enzyme, KAS I, is responsible

for producing varying chain lengths (616 carbons). Three additional reactions

occur after each condensation. To form a saturated fatty acid the 3-ketoacyl ACP

product is reduced by the enzyme

3-ketoacyl ACP reductase, dehydrated by hydroxyacyl ACP dehydratase and

then reduced by the enzyme enoyl ACP reductase (Figure 2). These four

reactions lead to a lengthening of the precursor fatty acid by two carbons. The

fatty acid biosynthesis pathway produces saturated 16:0- and 18:0-ACP. To

produce an unsaturated fatty acid, a double bond is introduced by the soluble

enzyme stearoyl ACP desaturase. The elongation of fatty acids is terminated

either when the acyl group is removed from ACP by an acyl-ACP thioesterase

that hydrolyzes the acyl ACP and releases free fatty acid or acyltransferases in

the chloroplast transfer the fatty acid directly from ACP to glycerol-3-phosphate

or monoacylglycerol-3-phosphate. The final fatty acid composition of individual

algae is determined by the activities of enzymes that use these acyl ACPs at the

termination phase of fatty acid synthesis.
http://file///media/UGAR_MOBILE/tesi/tesi.odthttp://file///media/UGAR_MOBILE/tesi/tesi.odt


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Figure 2. Fatty acid de novo synthesis pathway in chloroplasts.

Acetyl CoA enters the pathway as a substrate for acetyl CoA carboxylase (Reaction 1) as well

as a substrate for the initial condensation reaction (Reaction 3). Reaction 2, which is catalyzedby malonyl CoA:ACP transferase and transfers malonyl from CoA to form malonyl ACP.

Malonyl ACP is the carbon donor for subsequent elongation reactions. After subsequent

condensations, the 3-ketoacyl ACP product is reduced (Reaction 4), dehydrated (Reaction 5)

and reduced again (Reaction 6), by 3-ketoacyl ACP reductase, 3-hydroxyacyl ACP dehydrase

and enoyl ACP reductase, respectively (adapted from Ohlrogge and Browse 1995).


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2.4 Regulation of fatty acid Synthesis

All algae must produce fatty acids, and this synthesis must be tightly controlled

to the balance supply and demand for acyl chains. This need can be highly

variable and it depend on the stage of development, rate of growth and

surrounding factor as stress or nutrient deficiency[48], and therefore rates of

fatty acids biosynthesis must be closely regulated to meet these changes. Overall

fatty acids synthesis, and consequently its regulation may be more complicated,

unlike other organism, algae fatty acid synthesis , like plants, is not localized

within the cytosol but occurs in an organelle, the plastid, then most of the

amount is exported into the cytosol for glycerolipid assembly at the endoplasmic

reticulum (ER) or other sites. Both of the compartmentalization of lipid

metabolism and the intermixing of lipid intermediates in these pools present

special requirements for the regulation of the synthesis. A system for

communicating between the source and the sinks for fatty acids utilization is

essential. The nature of this communication and the signal molecules involved

remain an unsolved mystery in all vegetal.

Biochemists used different approaches to identify where the regulation occur in
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evidence supported this suggestion: Acetate or pyruvatewere incorporated into

acetyl-CoA in the dark by isolated chloroplasts, but malonyl-CoA and fatty acids

were formed only in the light(40). Thus, the light-dependent step of fatty acid

synthesis appeared to be at the Acetyl-CoA carboxylase reaction. Eastwell &

Stumpf (41) found that chloroplast and wheat germ Acetyl-CoA carboxylase

were inhibited by ADP and suggested this may account for light-dark regulation

of the enzyme. Nikolau & Hawke (42) characterized the pH, Mg, ATP, and ADP

dependence of maize Acetyl-CoA carboxylase activity and concluded that

changes in these parameters between dark and light conditions could account for

increased Acetyl-CoA carboxylase activity upon illumination of chloroplasts.

Finally, Acetyl-CoA carboxylase activity and protein levels are coincident with

increases and decreases in oil biosynthesis in developing seeds . However, in

vitro approaches are limited because they show only that the enzyme has in vitro

properties consistent with control, The sites of metabolic control of a pathway

can be more reliably identified by examination of in vivo properties of enzymes.

Although it is often technically difficult, examining the concentrations of the

substrates and products of each enzymatic step in a pathway provides

information on which reactions are at equilibrium and which are displaced from

equilibrium. This information is important because an essential feature of almost

all regulatory enzyme.


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Most of the substrates and intermediates of plant Fatty acids synthesis are

attached to acyl carrier protein (ACP) (Figure 2). Analysis of acyl-ACPs is aided

because the chain length of fatty acids attached to ACP alters the mobility of the

protein in native or urea PAGE gels. Because of these alterations in mobility,

most of the acyl-ACP intermediates of fatty acid synthesis can be resolved, and

when transferred to nitrocellulose, antibodies to ACP can provide sensitive

detection at nanogram levels. Although the acyl-ACP intermediates have a half

life in vivo of only a few seconds (43) (44), by rapidly freezing tissues in liquid

nitrogen it has been possible to determine the relative concentrations of free,

nonacylated ACPs and of the individual acyl-ACPs. Analysis of acyl- ACP pools

has been used to study regulation of fatty acids synthesis in spinach leaf and

seed (45) in chloroplasts in developing castor seeds and in tobacco suspension

cultures (46)(47) . The initial examination of the composition of the acyl-ACP

pools provided information about the potential regulatory reactions in plant fatty

acid biosynthesis. The various saturated acyl-ACP intermediates between 4:0

and 14:0 occur in approximately equal concentrations. Because the 3-ketoacyl-

ACPs, enoyl-ACPs, or 3-hydroxyacyl-ACPs, which are substrates for the two

reductases and dehydrase reactions, were not detected, it is likely that these

reactions are close to equilibrium and that the in vivo activities of these enzymes

are in excess. Thus it is not likely that these enzymes are regulatory. In contrast,

the concentration of acetyl-ACP was considerably above that of malonyl-ACP.


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This result suggests that the acetyl-CoA carboxylase reaction, which has an

equilibrium constant slightly favouring malonyl-CoA formation, is significantly

displaced from equilibrium and therefore potentially regulatory (43). The

condensing enzymes can also be considered displaced from equilibrium because

of the concentration of malonyl-ACP and the saturated acyl-ACPs. To obtain

more information on sites of regulation, the changes in pool sizes when flux

through the fatty acid biosynthetic pathway changes were examined. The rate of

spinach leaf fatty acid biosynthesis in the dark is approximately one sixth the

rate observed in the light (49).

In the light, the predominant form of ACP was the free, nonacylated form,

whereas acetyl-ACP represented about 56% of the total ACP (46). In the dark,

the level of acetyl-ACP increased substantially with a corresponding decrease in

free ACP, such that acetyl-ACP was now the predominant form of ACP. In

similar experiments, when chloroplasts are shifted to the dark, malonyl-ACP and

malonyl-CoA disappear within a few seconds, and acetyl-ACP levels increase

over a period of several minutes. The rapid decrease in malonyl-ACP and

malonyl-CoA when fatty acid synthesis slows, together with the increase in

acetyl-ACP and lack of change in other intermediate acyl-ACP pools all lead to

the conclusion that Acetyl-CoA carboxylase activity is the major determinant of

light/dark control over fatty acids synthesis rates in leaves.


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The above experiments on acyl-ACP and acyl-CoA pools have been carried out

with dicot plants. Gramineae species such as maize and wheat have a

substantially different (homodimeric) structure of Acetyl-CoA carboxylase. (50)

by a different approach toward evaluating metabolic control. They took

advantage of the susceptibility of maize and barley plastid Acetyl-CoA

carboxylase to the herbicides fluaxifop and sethoxidim. When chloroplasts or

leaves were incubated with herbicides and radiolabeled acetate, a flux control

coefficient of 0.5 to 0.6 was calculated for acetate incorporation into lipids. Flux

control coefficients of this magnitude indicate strong control by the Acetyl-CoA

carboxylase reaction over fatty acid synthesis (51). Thus, in a wide variety of

species and tissues, bothin vivo and in vitro experiments point ton Acetyl-CoA

carboxylase as a major regulatory point for plant fatty acid synthesis.

2.6 Feedback regulation

Most biochemical pathways are controlled in part by a feedback mechanism

which fine-tunes the flux of metabolites through the pathway. Whenever the

product of a pathway builds up in the cell to levels in excess of needs, the end

product inhibits the activity of the pathway. In most cases this inhibition occurs

at a regulatory enzyme which is often the first committed step of the pathway.

When the activity of the regulatory enzyme is reduced, all subsequent reactions


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are also slowed as their substrates become depleted by mass-action. Because

enzyme activity can be rapidly changed by allosteric modulators, feedback

inhibition of regulatory enzymes provides almost instantaneous control of the

flux through the pathway. It has long been considered that fatty acid synthesis is

partly controlled by feedback on Acetyl-CoA carboxylase by long-chain acyl-

CoAs. Since acyl-CoAs are one end product of the FAS pathway. Although this

inhibition seems logical, it has been called into question by the discovery that

acyl-CoAbinding proteins exist at high concentrations in the cytosol of animals

(52), yeast (53), and plants (54). Because these proteins have extremely high

affinity for acyl-CoAs, the concentration of free acyl-CoA in the cytoplasm may

be only nanomolar, a level unlikely to inhibit Acetyl-CoA carboxylase.

Several other potential feedback inhibitors such as acyl-CoA, free fatty acids,

and glycerolipids also fail to strongly inhibit the plant Acetyl-CoA carboxylase at

physiological concentrations (55). Because FAS occurs inside the plastid but the

major utilization of the products of fatty acid synthesis is at the ER membranes,

it is likely that feedback regulation must allow communication across the plastid

envelope. At this time we do not have any clear indications of what molecules

are involved in feedback regulation of plastid fatty acid synthesis.

2.7 What Controls Promoter Activity of FAS Genes?

A major challenge for the future is to discover how the level of expression of

genes of lipid synthesis are controlled. Efforts are under way to identify


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transcription factors that may bind to these elements in different organism. In

addition,computational and genetic approaches may allow identification of

additional controls.

Understanding how cells regulate the production of these fatty acids and direct

them toward their different functions is thus central to understanding a large

range of fundamental questions in algae biology. We now have convincing

evidence that Acetyl-CoA carboxylase is one enzyme that is involved in

regulating fatty acid synthesis rates, this is only the beginning. But what

molecules regulate Acetyl-CoA carboxylase by feedback or other mechanisms

and what metabolic signals or mechanisms control those molecules? We don't

have information about the nature of these controls. Thus, understanding

regulation of fatty acid synthesis is a rich and relatively unexplored field with

much work left to be done.


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3 State of the art

3.1 Comparison of lipid metabolism in algae and higher

plants

Although algae generally share similar fatty acid and TAG synthetic pathways

with higher plants, there is some evidence that differences in lipid metabolism

do occur. In algae, for example, the complete pathway from carbon dioxide

fixation to TAG synthesis and sequestration takes place within a single cell,

whereas the synthesis and accumulation of TAG only occur in special tissues or

organs (e.g. seeds or fruits) of oil crop plants. In addition, very long PUFAs

above C18 cannot be synthesized in significant amounts by naturally occurring

higher plants, whereas many algae (especially marine species) have the ability to

synthesize and accumulate large quantities of very long PUFAs, such as

eicosapentaenoic acid (C20:5 3), docosahexaenoic acid (C22:6 3) and

arachidonic acid (C20:4 6). Recently, annotation of the genes involved in lipid

metabolism in the green alga C.reinhardtii has revealed that algal lipid

metabolism may be less complex than in Arabidopsis, and this is reflected in the

presence and/or absence of certain pathways and the apparent sizes of the gene

families that represent the various activities (55)


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3.2 Factor affecting tryacilglycerolipids accumulation

and fatty acids composition

Although the occurrence and the extent to which TAG is produced appear to be

species/strain-specific, and are ultimately controlled by the genetic make-up of

individual organisms, oleaginous algae produce only small quantities of TAG

under optimal growth or favourable environmental conditions (57). Synthesis

and accumulation of large amounts of TAG accompanied by considerable

alterations in lipid and fatty acid composition occur in the cell when oleaginous

algae are placed under stress conditions imposed by chemical or physical

environmental stimuli, either acting individually or in combination. The major

chemical stimuli are nutrient starvation, salinity and growth-medium pH. The

major physical stimuli are temperature and light intensity. In addition to

chemical and physical factors, growth phase and/or aging of the culture also

affects TAG content and fatty acid composition.

3.2.1 Nutrients

Of all the nutrients evaluated, nitrogen limitation is the single most critical

nutrient affecting lipid metabolism in algae. A general trend towards


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accumulation of lipids, particularly TAG, in response to nitrogen deficiency has

been observed in numerous species or strains of various algal taxa, (58)

In diatoms, silicon is an equally important nutrient that affects cellular lipid

metabolism. For example, silicon-deficient Cyclotella cryptica cells had higher

levels of neutral lipids (primarily TAG) and higher proportions of saturated and

mono-unsaturated fatty acids than silicon-replete cells (59).

Other types of nutrient deficiency that promote lipid accumulation include

phosphate limitation and sulfate limitation. Phosphorus limitation resulted in

increased lipid content, mainly TAG, in Monodus subterraneus

(Eustigmatophyceae) (60) P.tricornutum and Chaetocerossp. (Bacillariophyceae),

and I.galbana and Pavlova lutheri (Prymnesiophyceae), but decreased lipid

content in Nannochloris atomus (Chlorophyceae) and Tetraselmis sp.

(Prasinophyceae) (61). Of marine species examined(61), increasing phosphorus

deprivation was found to result in a higher relative content of 16:0 and 18:1 and a

lower relative content of 18:4 3, 20:5 3 and 22:6 3. Studies have also shown

that sulfur deprivation enhanced the total lipid content in the green algae

Chlorella sp. and C. reinhardtii (62).

Cyanobacteria appear to react to nutrient deficiency differently to eukaryotic

algae. Piorreck in the (1996) investigated the effects of nitrogen deprivation on

the lipid metabolism of the cyanobacteria Anacystis nidulans, Microcystis


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aeruginosa, Oscillatoria rubescens and Spirulina platensis, and reported that

either lipid content or fatty acid composition of these organisms was changed

significantly under nitrogen-deprivation conditions. When changes in fatty acid

composition occur in an individual species or strain in response to nutrient

deficiency, the C18:2 fatty acid levels decreased, whereas those of both C16:0

and C18:1 fatty acids increased, similar to what occurs in eukaryotic algae . In

some cases, nitrogen starvation resulted in reduced synthesis of lipids and fatty

acids (63).

3.2.2 Temperature

Temperature has been found to have a major effect on the fatty acid composition

of algae. A general trend towards increasing fatty acid unsaturation with

decreasing temperature and increasing saturated fatty acids with increasing

temperature has been observed in many algae and cyanobacteria (64) . It has

been generally speculated that the ability of algae to alter the physical properties

and thermal responses of membrane lipids represents a strategy for enhancing

physiological acclimatization over a range of temperatures, although the

underlying regulatory mechanism is unknown (65). Temperature also affects the

total lipid content in algae. For example, the lipid content in the chrysophytan

Ochromonas danica (66) and the eustigmatophyte Nannochloropsis salina (67)


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increases with increasing temperature. In contrast, no significant change in the

lipid content was observed in Chlorella sorokiniana grown at various

temperatures (68). As only a limited amount of information is available on this

subject, a general trend cannot be established.

3.2.3 Light intensity

Algae grown at various light intensities exhibit remarkable changes in their gross

chemical composition, pigment content and photosynthetic activity (69).

Typically, low light intensity induces the formation of polar lipids, particularly

the membrane polar lipids associated with the chloroplast, whereas high light

intensity decreases total polar lipid content with a concomitant increase in the

amount of neutral storage lipids, mainly TAGs (70).

The degree of fatty acid saturation can also be altered by light intensity. In

Nannochloropsis sp., for example, the percentage of the major PUFA C20:5 3

remained fairly stable (approximately 35% of the total fatty acids) under light-

limited conditions. However, it decreased approximately threefold under light-

saturated conditions, concomitant with an increase in the proportion of saturated

and mono-unsaturated fatty acids (i.e. C14, C16:0 and C16:1 7) (71). Based

upon the algal species/strains examined (72), it appears, with a few exceptions,


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that low light favors the formation of PUFAs, which in turn are incorporated into

membrane structures. On the other hand, high light alters fatty acid synthesis to

produce more of the saturated and mono-unsaturated fatty acids that mainly

make up neutral lipids.

3.2.4 Growth phase and Physiological status

Lipid content and fatty acid composition are also subject to variability during the

growth cycle. In many algal species examined, an increase in TAGs is often

observedduring stationary phase. For example, in the chlorophyte Parietochloris

incise, TAGs increased from 43% (total fatty acids) in the logarithmic phase to

77% in the stationary phase (23), and in the marine dinoflagellate Gymnodinium

sp., the proportion of TAGs increased from 8% during the logarithmic growth

phase to 30% during the stationary phase. Coincident increases in the relative

proportions of both saturated and mono-unsaturated 16:0 and 18:1 fatty acids

and decreases in the proportion of PUFAs in total lipid were also associated with

growth-phase transition from the logarithmic to the stationary phase. In contrast

to these decreases in PUFAs, however, the PUFA arachidonic acid (C20:4 6) is

the major constituent of TAG produced in Parietochloris incise cells (23) while


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docosahexaenoic acid (22:6 3) and eicosapentaenoic acid (20:5 3) are

partitioned to TAG in the Eustigmatophyceae N.oculata, the diatoms

P.tricornutum and T.pseudonana, and the haptophyte Pavlova lutheri (73).

Culture aging or senescence also affects lipid and fatty acid content and

composition. The total lipid content of cells increased with age in the green alga

Chlorococcum macrostigma, and the diatoms Nitzschia palea, Thalassiosira

fluviatillis and Coscinodiscus eccentricus . An exception to this was reported in

the diatom P.tricornutum, where culture age had almost no influence on the total

fatty acid content, although TAGs were accumulated and the polar lipid content

was reduced (74). Analysis of fatty acid composition in the diatoms

P.tricornutum and Chaetoceros muelleri revealed a marked increase in the levels

of saturated and mono-unsaturated fatty acids (e.g. 16: 0, 16:1 7 and 18:1 9),

with a concomitant decrease in the levels of PUFAs (e.g. 16:3 4 and 20:5 3)

with increasing culture age (75). Most studies on algal lipid metabolism have

been carried out in a batch culture mode. Therefore, the age of a given culture

may or may not be associated with nutrient depletion, making it difficult to

separate true aging effects from nutrient deficiency-induced effects on lipid

metabolism.


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3.2.5 Physiological roles of triacylglycerol

accumulation

Synthesis of TAG and deposition of TAG into cytosolic lipid bodies may be,

with few exceptions, the default pathway in algae under environmental stress

conditions. In addition to the obvious physiological role of TAG serving as

carbon and energy storage, particularly in aged algal cells or under stress, the

TAG synthesis pathway may play more active and diverse roles in the stress

response. The de novo TAG synthesis pathway serves as an electron sink under

photo-oxidative stress. Under stress, excess electrons that accumulate in the

photosynthetic electron transport chain may induce over-production of reactive

oxygen species, which may in turn cause inhibition of photosynthesis and

damage to membrane lipids, proteins and other macromolecules. The formation

of a C18 fatty acid consumes approximately 24 NADPH derived from the

electron transport chain, which is twice that required for synthesis of a

carbohydrate or protein molecule of the same mass, and thus relaxes the over-

reduced electron transport chain under high light or other stress conditions. The

TAG synthesis pathway is usually coordinated with secondary carotenoid

synthesis in algae(76). The molecules (e.g. -carotene, lutein or astaxanthin)

produced in the carotenoid pathway are esterified with TAG and sequestered into

cytosolic lipid bodies. The peripheral distribution of carotenoid-rich lipid bodies

serve as a 'sunscreen' to prevent or reduce excess light striking the chloroplast


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under stress. TAG synthesis may also utilize PC, PE and galactolipids or toxic

fatty acids excluded from the membrane system as acyl donors, thereby serving

as a mechanism to detoxify membrane lipids and deposit them in the form of

TAG.

3.3 Algae genomic and proposed model system in biofuel

production

Some eukaryotes genome have been sequenced. These eukaryotes include C.

reinhardtii and Volvox carteri (green alga), Cyanidioschizon merolae (red alga),

Osteococcus lucimarinus and Osteococcus tauris (marine pico-eukaryotes),

Aureococcus annophageferrens (a harmful algal bloom component), P.

tricornutum and T. pseudonana (diatoms) (table 1).

Many effort have been done to sequenced diverse eukaryotic algae that represent

a diverse group of organisms and at the time many project are in progress to

improve the genetic knowledge but the only organism among the latter for

which extensive genomic, biological and physiological data exist is C.

reinhardtii, a unicellular, water-oxidizing green alga (77). Among these ,

Chlamydomonas has been used as a model eukaryote microbe for the study of

many processes, including photosynthesis, phototaxis, flagellar function,


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nutrient acquisition, and the biosynthesis and functions of lipids. The advantage

of C.reinhardtii as a model for oxygenic photosynthesis derives mainly from its

ability to grow either photo-, mixo- or heterotrophically (in the dark and in the

presence of acetate) while maintaining an intact, functional photosynthetic

apparatus. This property has allowed researchers to study photosynthetic

mutations that are lethal in other organisms. Moreover, C.reinhardtii spends most

of its life cycle as a haploid organism of either mating type + or .

Gametogenesis is triggered by environmental stresses, particularly nitrogen

deprivation , and its occurrence can be synchronized by light/dark periods of

growth. During its haploid stage, C. reinhardtii can be genetically engineered

and single genotypes easily generated. Additionally, different phenotypes can be

obtained by crossing two haploid mutants of different mating types carrying

different genotypes. Conversely, single-mutant genotypes can be unveiled by

back-crossing mutants carrying multiple mutations with the wild-type strain of

the opposite mating type.

Global expression profiling of Chlamydomonas under conditions that produce

biofuels (H2

in this case) (78) has been reported using second-generation

microarrays with 10 000 genes of the over 15 000 genes predicted (77). Much of

the information that was reported involves fermentative metabolism. No

concerted effort to characterize up- and downregulation of genes associated with


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lipid metabolism when Chlamydomonas is exposed to nutrient stress has yet

been reported. Nevertheless, N-deprived C.reinhardtii will over-accumulate

starch and lipids that can be used for formate, alcohol and biodiesel production

(78).

Procedures for metabolite profiling of C. reinhardtii CC-125 cells, which quickly

inactivate enzymatic activity, optimize extraction capacity, and are amenable to

large sample sizes, were reported recently (79). Sulfur, Nitrogen-, phosphate-

and iron-deprivation profiles were examined, and each metabolic profile was

different. Sulfur depletion leads to the anaerobic conditions required for

induction of the hydrogenase enzyme and H2

production (80). Rapidly sampled

cells (cell leakage controls were determined by 14C-labeling techniques) were

analyzed by gas chromatography coupled to time-of-flight mass spectrometry,

and more than 100 metabolites (e.g. amino acids, carbohydrates, phosphorylated

intermediates, nucleotides and organic acids) out of about 800 detected could be

identified. The concentrations of a number of phosphorylated glycolysis

intermediates increase significantly during sulfur stress (79), consistent with the

upregulation of many genes associated with starch degradation and fermentation

observed in anaerobic Chlamydomonas cells(78). Unfortunately lipid

metabolism was not studied. Finally, researchers are starting to ask whether

Chlamydomonas and other green algae have the required metabolic pathways to


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produce other energy-rich products such as butanol.

Chlamydomonas proteomics is in its infancy, but there have been a number of

relevant studies, as reviewed by (81). However, to our knowledge, no proteomics

research has yet been reported in algae under biofuel-producing conditions.

Genus species Sequencing status reference

green algae

Chlamydomonas reinhardtii JGI genome project

Chorella spNC64A JGI genome project

Chorella vulgaris in progress genbank

Ostreococcus lucimaris JGI genome project

Ostreococcus tauri JGI genome project

Volvox carteri JGI genome project

Scenedesmus obliquus minimal genbank

Dunaliella salina minimal genbank

diatom

Phaeodactylum tricornutum JGI genome project

Thalassiosira pseudonana JGI genome project

red algae

Cyanidioschyzon merolae complete

brown algae

Aureococcus anophagefferens JGI genome project

complete assembly release,

v 3.0

complete assembly release

v 1.0


v.2.0Complete assembly release

v2.0,

Complete assembly release

v1.0,


v2.0complete assembly release

v 3.0

Cyanidioschyzon merolae

project

Complete assembly release

v1.0,
http://www3.interscience.wiley.com/cgi-bin/fulltext/120090055/main.html,ftx_abs#b132http://www3.interscience.wiley.com/cgi-bin/fulltext/120090055/main.html,ftx_abs#b132


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3.4 Acetil CoA carboxylase protein and genetic

characterization

To characterized the first committed step in the fatty acid biosynthetic pathway,

different study have been conducted to seek for Acetyl-CoA carboxylase but just

in few species it is know .

The gene that encodes Acetyl-CoA carboxylase in Cyclotella cryptica has been

isolated and cloned [82]. The gene was shown to encode a polypeptide

composed of 2089 amino acids, with a molecular mass of 230 kDa. The deduced

amino acid sequence exhibited strong similarity to the sequences of animal and

yeast Acetyl-CoA carboxylases in the biotin carboxylase and carboxyltransferase

domains. Less sequence similarity was observed in the biotin carboxyl carrier

protein domain, although the highly conserved Met-Lys-Met sequence of the

biotin binding site was present. The N-terminus of the predicted Acetyl-CoA

carboxylase sequence has characteristics of a signal sequence, indicating that the


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enzyme may be imported into chloroplasts via the endoplasmic reticulum. Also

the protein has been purified and kinetically characterized from two unicellular

algae, the diatom Cyclotella cryptica [82] and the prymnesiophyte Isochrysis

galbana [83]. Native Acetyl-CoA carboxylase isolated from Cyclotella cryptica

has a molecular mass of approximately 740 kDa and appears to be composed of

four identical biotin-containing subunits. The molecular mass of the native

Acetyl-CoA carboxylase from I.galbana was estimated at 700 kDa. This

suggests that Acetyl-CoA carboxylases from algae and the majority of Acetyl-

CoA carboxylases from higher plants are similar in that they are composed of

multiple identical subunits, each of which are multi-functional peptides

containing domains responsible for both biotin carboxylation and subsequent

carboxyl transfer to acetyl CoA [83].

Investigated changes in the activities of various lipid and carbohydrate

biosynthetic enzymes in the diatom Cyclotella cryptica in response to silicon

deficiency. The activity of Acetyl-CoA carboxylase increased approximately

two- and fourfold after 4 and 15 h of silicon-deficient growth, respectively,

suggesting that the higher enzymatic activity may partially result from a covalent

modification of the enzyme. As the increase in enzymatic activity can be

blocked by the addition of protein synthesis inhibitors, it was suggested that the

enhanced Acetyl-CoA carboxylase activity could also be the result of an increase

in the rate of enzyme synthesis[82].


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No more experimental result are available about the sequence of this gene, but

from the algae's genome know some sequence are predicted by comparison with

other species

4 Methods

4.1 Genetic source

The purpose of this thesis is to analyse the gene (ACCase) and the amount of

fatty acids in algae. Thus the main source of information is represented by

amount of fatty acids, (TAGs), predicted genes and related information on them.

As mentioned above, only one gene from Cyclotella cryptica has been

experimental characterized. To retrieve the most part of this information we

used different databases, principally AlgaeBase and Joint Genome Institute

databases, although Genebank and EMBL- BANK were also used. A brief

description of the less known databases are give below.

AlgaeBase is a database of information on algae that includes terrestrial, marine


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and freshwater organisms. At present, the data for the marine algae, particularly

seaweeds, are the most complete and also include sea-grasses. Unfortunately it

is also a work in progress and much of the data is incomplete. This database was

used particularly to collect information about the quantity of fatty acids and

(TAGs) from previously published articles.

JGI is a comprehensive database of the genomes of Eukaryotic species and it

has the most complete genomic data of algae strains, data is organized following

the scheme whereby the main object is the genome; this is organized into

portions of the genomic sequence reconstructed from the end sequence , called

the scaffold. They are composed of contigs and gaps. One chromosome may be

represented by many scaffolds, depending on the extend of the genome

information. The database holds a list of prediction genes that can be retrieved

by browser (KOG) EuKaryotic Orthologous Group. Using a KOG tool for a JGI

sequence organism provides a way to find predicted genes by functional

classification or identified number as well as protein sequence and related. the

genes are referred to using Internal and external cross-referenced annotations.

The data was found using a query on the KOG browser by functional KOG

database and by following the cross reference annotation to NCBI genebank

stored in local.More details are available in the (table 2)


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Table2:of gene sequence and quantity of fatty acids (*cDNA sequence; ** besed on proteine

aligment

Genus species Beta Biotin Lipids

G ID:5722616 ID:5728708 ID:5727859 18-24% DW NCBI

R ID: 36222 ----- ----- 29% DW GJI

E ----- ----- ----- 15-55% DW

E ID: 106840* ID: 82311 ----- ----- GJI

N ID:EC187118** ----- ----- ----- EBI

----- ----- ----- 45% DW

-----

----- ----- ID:4999505 ----- NCBI

----- ID:EF363909* ----- ----- EBI

----- ----- ----- 36-42% DW

----- ----- ----- 33% DW

----- ----- ----- 28-40 DW

----- ----- ----- 29-75% DW

----- ----- ----- 35% DW

D ----- ----- ID: 6770 21-31% DW GJI

I 21% DW

A-----

----- ----- 42% DW

T-----

----- ----- 28-50% DW

O-----

----- ----- 11-31% DW

M ----- ----- ID:55209 11-31% DW GJI

Alpha Source

Clamidomonas reinhardtii

Chorella spNC64A

Chorella Protothecoides Xu et al. 2006

Volvox Carteri

Scenedesmus obliquus

Scenedesmus TR84 Sheehan et al.1998

Ostreococcus TauriLocus :CR954208

NCBI GenBank

Ostreococcus Lucimaris

Dunaliella Salina

Dunadiella Tertioleca Kischimoto et. al.1994

Sticoccus sheehan et al.1998

Ankistrodesmus TR84 Tornabene et al. 1998

Botryococcus Braunii Metzger et al. 2005

Parietochloris Incisa Roessler (1988)

Thalassiosira Pseudonana

Cyclotella CripticaLocus :L20784

NCBI GenBank

Cyclotella Di-35 Sheehan et al.1998

Nitzschia TR-114 Kyle DJ, et al. 1991

Hantzschia DI 160 Sheehan et al.1998

Phaeodactylum Triconutum


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4.2 Sequence analysis

In order to find a data set as reliable as possible, different strategies were applied

these depended on the database employed. Nevertheless, in most cases the use of

a Basic Local Alignment sequence tool (Blast) in comparison to the nucleotide

or protein resulted in being able to see what was relevant and of particularly

importance. BLAST uses statistical theory to produce a bit-score and expect

value (E-value) for each alignment pair. The E-value gives an indication of the

statistical significance of a given pairwise alignment and reflects the size of the

database and the scoring system used. The lower the E-value, the more

significant the hit. A sequence alignment that has an E-value of 0.05 means that

this similarity has a 5 in 100 (1 in 20) chance of occurring by chance alone. A

strict, high E-value threshold (1-80) has been applied in order to keep the most

reliable sequences. Following these strategies we managed to seek out ACCase

predicted genes and some sequences that point out high E-value beyond the

threshold, furthermore to evaluate meaningful sequences ClustalW in local was

used. This perform a global-multiple sequences alignment by the progressive

method with the following steps: perform pair-wise alignment of all sequences

by dynamic programming, use the alignment scores to produce a phylogenetic


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tree by neighbour-joining, align the multiple sequences sequentially guided by

the phylogenetic tree, thus, the most closely related sequences are aligned first,

then additional sequences and then groups of sequences are added. These are

guided by initial alignment shown in each column of the sequence variations

among the sequences.

Using Clustalw it was possible to discriminate between the sequences in the

family groups Diatom and Green algae and highlight the difference between

them in the ACCase sequences, in different sub-sequence of the same gene

called ( alpha, Beta and Biotin) in accordance with the predicted sequences as

shown in table2. However, it was useful to discard those sequences that showed

a high E-value and lower similarities with each other.

The best matches were determined in different stages refining the alignments by

setting up the Clustalw's parameters like: slow- Accurate alignment, cost of the

gap, and the IUB DNA weight matrix.

4.3 Computational methodologies


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The available methods that are more commonly used first build an alignment,

either local or global, of the sequences investigated, or to take advantage of the

pre-computed full genomic alignments now available. One simple solution is to

identify conserved functional elements by using descriptors of the binding

specificity of TFBSs I.e position specific weight matrices, provided, for

example, in the TRANSFAC database, and also to look for conserved aligned

regions fitting the descriptor. This approach can be used for the detection of

single TFBSs.

Methods of this kind need reliable descriptors of the binding specificity of the

different TFs. Usually, PWMs yield a large number of false positive matches and

in requiring a match to be conserved throughout different sequences it is

possible to reduce them, however, the problem of defining whether a match is

significant in all the species considered remains. Secondly, and more

importantly, is the need to have a reliable alignment of the sequences

investigated. TFBSs tend to be quite short (815 nucleotides), in comparison

with a normally analysed region of 5001000 bp, in these cases the sequences

aligned are too divergent resulting in the possibilities that conserved TFBSs

may be missed simply because they are not aligned correctly.

To overcome this, different solution were identified : Bioprospector [85] and

Galf_P [86]were used. Both of them used a heuristic method and both seek for

TFBSs without any prior preset knowledge, a brief description of both programs

is given in the following paragraphs.


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4.3.1 Bioprospector

This examines the upstream region of genes in the same gene expression pattern

group and looks for conservative sequence motifs. BioProspector uses zero to

third-order Markov background models, whose parameters are either given by

the user or estimated from a specified sequence file. The significance of each

motif found is judged based on a motif score distribution .

It takes the following input parameters; a file where the flanking region are

stored, a file with the background distribution using calculations on the bases of

the input file and the widths of the motif. At the end of the bioprospector run the

following results can be obtained : The motif score, significance value and the

number of the significance alignments segments. A regular expression of the

motif consensus and degenerates, as well as a probability matrix expression of

the motif. The number of segments each input sequence contributes to the motif,

the starting position and the sequence of each segment.

Within each run of BioProspector, a process called threshold sampler is

performed a number of times. Threshold sampler initializes a motif probability

matrix by a random alignment of the input sequences and improves the matrix

iteratively and stochastically

4.3.1.1Scoring segment with background Markov

dependency

Every possible segment of width w within a randomly chosen sequence s (in

input file) is considered. A score Ax = Qx / Px is computed and a new


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both efficiency and accuracy can be achieved.

4.3.2.1Representation

For an individual, the basic representation is the position- led one, which is an

array storing the starting position in each sequence. Starting from each position

in an individual, a subsequence with the motif width can be extracted and is

called a motif instance. The consensus of an individual is represented by a

Position-specific Weight Matrix (PWM) generated from the motif instances.

Each cell in the PWM indicates the normalized frequency of the nucleotide in a

particular position of the motif instances

4.3.2.2Fitness Evaluation

The fitness function adopt for each individual is its information content. For

each position i in the extracted motif instances, the positional information

content is :


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where fb is the observed frequency of nucleotide b on the column and pb is the

background frequency of the same nucleotide. The summation is taken over the

four possible types of nucleotides (b {A, T, C, G}). And the fitness is the sum

of positional information content which has the following form

where W is the motif width.

Though known regulatory motifs do not always have the highest information

content at every base position, the sum of positional information content is still a

good measure to reflect the overall conservations since, for the moment, no

completely satisfactory measurement exists.

As for the consensus representation, the similarity score is used to pick out those

false positives of motif instances from a position-led individual. The instance

similarity is calculated as the sum of the score of each corresponding letter in

the PWM of the consensus:

where bi is the nucleotide in position i of the motif instance, and PWM (bi , i) is


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the score of bi at position i in the PWM.

4.3.2.3Selection and Genetic Operators

Binary tournament is employed for parent selection. In particular, when

choosing a parent, it is randomly picked up by two individuals and the one with

higher fitness is chosen. The purpose is to maintain appropriate selection

pressure under which some of the currently unfit individuals have the chance to

reproduce and this may yield robust offspring in further generations. For

reproduction, single-point mutation for a single parent and single-point crossover

for double parents are applied. Mutation and crossover are performed with a

total probability of 1. While mutation is chosen, one of the positions of the

single parent will be shifted randomly. While crossover is applied, a crossover

point is chosen at random from [1, SeqNum 1], where SeqNum is the number

of sequences. Then the segments of the two parents after the crossover point are

swapped, yielding two children. One of them will be chosen at random as the

offspring. After reproduction to generate offspring, the population is increased

by a half for replacement.

4.3.2.4Local filtering Operator


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One of the feature operators in GALF is the local filtering operator, which can

filter out the false positives in a position-led individual in terms of the motif

instances' similarities to the consensus represented by PWM.

Firstly, the motif instances within an individual is ranked by their similarity

scores to the consensus. Secondly, the sequence containing the instance with the

lowest similarity score is scanned. Among all the possible starting sites of the

instance, the one giving the best similarity to the consensus is chosen. If the rank

does not change, which means this best instance is not, in fact, better than its

original preceding instance based on the other sequence in terms of similarity

score. In this case the local filtering stops, otherwise the preceding instance

becomes the worst, the sequence containing it is then selected and scanned as in

the first step. This is iterated until the rank does not change or the sequence

containing the original second best instance is scanned. It is notice that the

PWM will not be updated before the local filtering is finished for two purposes;

one is to save the computational load compared with the on-line update, the

other purpose is to try not to be too greedy. In order to keep the contribution of

evolutionary process, the filtering operator is only triggered once after certain

generation intervals and applied to those only newly generated

4.3.2.5Replacement Strategy


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Replacement is applied to keep the population size constant after the increase of

individuals during reproduction. Before replacement, all duplicate individuals

will be removed to avoid a take-over rate that is too fast . This is done by

assigning an arbitrarily low fitness to those duplicates. Each individual competes

with K randomly chosen from other individuals and scores a win if its fitness is

higher than its competitor. K is user defined and fixed at 10 . The number of

wins of each individual are recorded and ranked, when there is a tie in the

number of wins between the two different individuals. They are then re-ranked

by their fitness. Those whose final rankings are beyond the desired population

size will be eliminated.

4.3.2.6Shift Operator

When the individual with best fitness stagnates, which means it does not change

after certain generations ( the generations of stagnation), a small number of

shifts (all of the positions of the individual are moved in either

direction by the same bases) are tested for improvement of fitness. Based on the

gain of fitness, the smallest shift with positive gain will be chosen. If both

directions of the same shift number achieve improvement, the direction with

better gain is chosen. This moderate shift operator is to prevent a drastic shift

which may drag the solutions to local optima too fast before convergence.


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Table 4: example of output

Unfortunately the alignments did not give reliable primers because the

alignments were not specific enough to design a primer from them,

consequently protein sequences were tried in order to find a quality alignment

and retrieve from it a cDNA sequence. However this strategies did not give good

results. The last step was to scan the EST database using BLAST which gave

%GC GAP

GTATCGGCAGCATCAATGG 19 52,63 58 15 3 4002 772 ATG(16)

GTCACTATTGATGTCTCATCT 21 38,09 58 1 4002 4777 stop*

ACGAGCGTTGAGCTCAACG 19 57,89 60 3 4279 382 TGA(9)

CTTCTTGTCTTCGTACGGTT 20 45 58 1 4279 4661 ATG(14)

GTATCGGCAGCATCAATGG 19 52,63 58 5 3889 772 ATG(16)

CTTCTTGTCTTCGTACGGTT 20 45 58 1 3889 4661 ATG(14)

GTGCGTTGCCACGAGCGT 18 66,66 60 13 0 3920 372 N. start

CGCGAACCGACCCGCG 16 81,25 58 11 1 3920 4292

ATCGGCAGCATCAATGGCA 19 52,63 58 13 2 2950 774 ATG(14)

CCCACCGTGCGCGCCA 16 81,25 58 12 1 2950 3724

TAGCGATAATGAGAGCAGGG 20 50 60 12 1 2442 853 start/ stop*

GCCCTCATTCACTTGCGTG 19 57,89 60 13 0 2442 3295 TGA(11);TAA(7)

TCGCCGCAACTTCGGCAT 18 61,11 58 12 0 1796 1391 N. start

GCTCCTCCTCTAGTTCGAC 19 57,89 60 12 0 1796 3187

TAACCCACTTGAGGAGCAC 19 52,63 58 12 0 4794 24 TGA(10);TAA(1)

CCAGGTTCCGACACATGC 18 61,11 58 11 1 4794 4821 O.Tauri2

CGCGTGCGTGGCCGCG 16 87,5 60 11 1 4441 243 O.Tauri2 N. start

CCAACGTCGTACCACGCG 18 66,66 60 14 0 4441 4684 ATG(10)

TAAGCGCATCAAGGAGGTG 19 52,63 58 13 2 4229 356 O.Tauri2 TAA(1)

CACTGTGCTGACAGCTGTT 19 52,63 58 14 1 4229 4585 TGA(2)

ATGCTCCCTGTGGGCACA 18 61,11 58 12 1 4152 402 O.Tauri2 ATG(0)

TGGGTGCTGGGCGGGC 16 81,25 58 11 0 4152 4554

TGACGAAGACTCAGATTGTAT 21 38,09 58 14 1 3911 558 O.Tauri2 TGA(1)

CGACCGTCCCACGGCTC 17 76,47 60 13 1 3911 4469

GCGTCTGCAAGTCGCTCG 18 66,66 60 14 0 3549 647 O.Tauri2 N. start

CCGCCTCGTTATTGCTTAC 19 52,63 58 13 2 3549 4196 ATG(17);TAA(11)

TTTCGCGCCAAAAGCTATCT 20 45 58 13 1 2776 1170 O.Tauri2 N. start

GGGGGCGGTCGTCGTTG 17 76,47 60 13 1 2776 3945

CCCACTTGAGGAGCACCT 18 61,11 58 12 0 4697 27 O.Tauri3 TGA(6)

GTAACGGGTTCTCGTCTATG 20 50 60 13 0 4697 4724

CACCACGACGCTTGAGTTT 19 52,63 58 14 2 4008 314 TGA(13)

GCGAAAGTACCGACCGCG 18 66,66 60 12 2 4008 4322 ATG(8)

TGGATCTGGAGCAATTGGC 19 52,63 58 15 2 3615 536 N. start

GGCCGAGCGGTCGCGG 16 87,5 60 12 1 3615 4151

AGCACCAGCTGGCGGGA 17 70,58 58 13 2 3030 930 N. start

TTGCCGCGGCAGCAGTTG 18 66,66 60 12 1 3030 3960

Primer design

ipotetical primer lenght Cmelting Pb conservative fragment position alligment whit strat/ stop

Forward 5'-3' cyclotella Cryptca

Reverse 3'-5'

Forward 5'-3'

Reverse 3'-5'

Forward 5'-3' O.Tauri

Reverse 3'-5' N.stop

Forward 5'-3'


Forward 5'-3'

Reverse 3'-5'

Forward 5'-3'


Forward 5'-3'


Forward 5'-3'

Reverse 3'-5'

Forward 5'-3'

Reverse 3'-5'

Forward 5'-3'


Forward 5'-3'

Reverse 3'-5'

Forward 5'-3'

Reverse 3'-5'

Forward 5'-3'


Forward 5'-3'


Forward 5'-3'

Reverse 3'-5'

Forward 5'-3'


Forward 5'-3'



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some sequences, one of which was from Scenedesmus Obliquos .

see table 2 and the alignment below :


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From that alignments it was possible to design some perfect matches and

degenerate primer sequences as shown below :

SceneForward 5'ACCTGCCTGGACATCATCCTNAACATCAC 3' Tm=64 GC=51,7 no dimer

Scene Reverse 5'TACCGGAGCGGGACCGGGTCGA 3' Tm=68 GC=~72 no dimer


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reindhardtii Forward 5' ATCGGCCACCAGAAGGGC 3' Tm= 62 GC=66.6 no dimer

reindhardtii Reverse 5' CGTGGCGCATGAAGCGCA 3' Tm= 60 GC=66.6 no dimer

4.5 Experimental procedure

Organism Scenedesmus Protuberans was obtained from (Ege Biotecnology), to

grow the algae two culture mediums based upon Provasoli and Guillard (f/2)

recipes were prepared, that were used with a range of strains ( Chlamidomonas


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the algae growing rate being too slow. To eliminate this unknown contamination

the antibiotic as Spectinoycin was added, however this was not successful. An

extraction of DNA by (Qiagen KIT ) was attempted from the culture but as was

predicted beforehand the algae DNA was not obtained as shown in the photo of

electrophoresys analysis below.


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Using this sample two different PCR analysis were obtained which identified

this sequence.


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The first one, PCR analysis, was conducted with the following thermal cycle,

initial denaturation 94 C for 2 min, denaturation 94 C for 20 sec , Annealing

61,5 C 10 sec, extention 72 C 15 sec final extention 72C 5 min. The cycle was

repeat 30 times. According to primer temperature and the length of the expected

product

The second one, was carried out with a gradient PCR and a different primer

reverse (Rcre), in this way it was possible to optimised the melting temperature

and avoid unspecific product. The reaction was conducted with 8 samples at

different temperatures, calculated from the average annealing temperature 58.5

C as show in the picture :


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The next analysis was used to check if a digestion by restriction enzyme gave

the expect fragment by gel purification of the last 3 upper bands from the last

gradient PCR done. A SphI was used for this in accordance with the current


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assets of the laboratory and the restriction site on the map, having just one on

the sequence. Unfortunately no negative control was possible due to the lack of

enzyme

5 Result and future perspective

Biodiesel produced from sea algae would create an alternative fuel source

without the necessity to displace any land currently used for the production of

food, it would also require the creation of many new jobs in the alga-culture

industry. For theses reasons algae was chosen as the biological system for this

project. Due to the fact that research has not yet fully defined algae the projects

first consideration was to create a database for this information. The available

data was used in an attempt to identify the Transcription Factor Binding Site

(TFBSs). These short sequences have a fragment length usually 8-15 bp in the

cis-regulatory region and can regulate gene expression by the interaction of the

Transcription Factor (TFs). These are usually 100-300 bp upstream region of the

transcriptional start site of the gene. In higher eukaryotes they can be found

upstream, downstream or in the introns of the genes that they regulate.

Furthermore, they can be close or far away from the regulated genes. TFBSs are

a crucial component for gene regulation that affects the Transcription Processes


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and the final phenotypes of an organism. Typically, when certain TFs bind to the

TFBS in the promoter region of the corresponding sequence, the transcriptional

process is signalled and initiated. On the other hand, when other competing

molecules interacted with the binding site the transcription factor fails to bind

and the transcriptional process, in the worst case scenario, was inhibited The

molecule and hence the TFBS can cause modulation in the transcriptional

process which in turn produces more or less mRNA. Therefore it was necessary

to identify the TFBSs sequence to be able to decipher the mechanism of gene

regulation. Based upon this a hypothesis was made to prove that algae with a

high fatty acid content would have stronger promoters for ACCase genes.

Therefore it was necessary to be able to indentify a sequence which conserved

the common factors for those strains which produce a significant quantity of

fatty acids that might be lacking in other strains. Unfortunately due to the

limitations of the genomic data and a lack of quantified data in respect of the

quantity of fatty acids in algae currently available it was not possible to carry out

this study. Instead the upper part of the alpha fragment homologous predicted

gene was used to search for the generic conservative sequence was shared. To

explore this field a bio-informatics approach was used; although a biological

experiment method such as DNA foot printing and gel electrophoresis would

have been more reliable and accu

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