School of Biological Sciences

BS3010INDIVIDUAL PROJECT

SUMMER/TERM 1 TERM 1/TERM 2

FULL PROJECT TITLE: Potential role(s) of genes identified by representational difference analysis in the regulation of adult skeletal muscle stem cell quiescence.

WORD COUNT:

STUDENT NAME: Patrick Hurley

STUDENT ID NUMBER: 100767645

RHUL EMAIL ADDRESS: zava773@live.rhul.ac.uk

School of Biological Sciences, Royal Holloway, University of London, Egham, Surrey TW20 0EX

PROJECT SUPERVISOR: Dr BeauchampWet Project Dry Project

TURNITIN RECEIPT NUMBER: 49942320

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2015-16

8230

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CONTENTS Title page…….……………………………………….…………………………………………...1

Abstract………………………………..…………..………………………………………………3

Introduction…………………………………….…………………………………………………4

Data source and analytical methods……….……………………………………………………...9

Results……………..…………….………..……………………………………………...………13

Discussion…………………….……………..………………………………………………......26

Acknowledgments………….…………………..………………………………………………..31

References……………………………………..………………………………………….……..32

Appendix i…………….…………………………….……………………………………………41

Forms……………………………...……………………………………………………………..45

Data CD………………………….……………………………………………………………..47

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ABSTRACT

Long non-coding RNA is gradually becoming more accepted as not merely transcriptional

noise but as a crucial regulatory component of correct genomic function. Nowhere is this

more pronounced than in the study of cell differentiation and lineage commitment of

progenitor stem cells. The aim of this project was to use bioinformatics tools to attempt to

elucidate the role a nucleotide sequence with no protein coding capability performs in the

process of satellite stem cell quiescence. This query sequence was identified using

representational difference analysis to amplify the differences in the genomes between a

quiescent and activated satellite cell. The results indicate that the sequence was part of an

lncRNA molecule that may be working with miR-883a-5p and CTCF transcription factor to

repress expression of the Mmd gene, or as a competing endogenous factor to the same

microRNA to fulfil a similar function. At the end of this paper are details of laboratory

techniques which could be used to further study this theory, these include a combination of

PAR-CLIP and CHIRP-SEQ technologies.

N. B. The photo on the title page shows a Circos plot comparing density of: SNPs, SVs and

transposable elements amongst four wild strains of mice (Keane et al., 2011).

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INTRODUCTION

Satellite cells were first observed by Alexander Mauro in 1961 through electron microscopy

of the periphery of the tibialis anticus muscle in a frog. He commented even at this time that

due to their intimate association with the muscle that they may play an important role in the

‘vexing problem of skeletal muscle regeneration’ (Mauro, 1961). Their incredible

regenerative capabilities however were first really studied by Studitsky in 1964. This was

achieved through removal and decimation of frog muscles to an unrecognisable state.

Remarkably, from this ‘semi-porridge’ state of cells, reconstruction of striated muscle did

occur. This led Studitsky to conclude that there must be cells held within the tissue capable of

developing entirely new muscle fibres (Studitsky, 1964).

Since then the field of satellite cell research has progressed greatly. It is now widely believed

that these satellite cells are descendants of embryonic cells left non-terminally differentiated

to act as progenitors in the muscle after development has been completed (Seale & Rudnicki,

2000; Yu & Rudnicki, 2011). A vital point in understanding satellite cells is that they are

capable of self-renewal so as to maintain a pool of available cells ready to be activated

following muscle damage (Yoshida et al., 1998; Verdijk, 2014). This ability is thought to be

overwhelmed in the satellite cell pools of patients suffering from muscular dystrophy,

resulting in a lack of muscular recovery and pathological muscle fibrosis and atrophy (Heslop

et al., 2000).

Satellite cells are located in a niche between the basal lamina and underlying myofibre

sarcolemma. In this position they are able to receive notch signalling from the myofibre

below (Bischoff, 1990) and integrin signalling (especially α7β1 integrin) from the basal

lamina above (Kuang et al., 2008; Zammit, 2008). From this site, they can progress in a clear

manner from muscle satellite cell to myoblast, after which there is a choice between fusing

together to form new myotubes or joining into pre-existing myofibres to become post mitotic

myonuclei within the syncytium that is a muscle fibre (LaBarge & Blau, 2002). This niche is

considered vital as the cells themselves are asymmetrically distributed as a heterogeneous

population dependent on communication for determination of cell fate, this can lead to either

self-renewal of the cell pool or re-entering of the cell cycle following injury (Kuang et al.,

2007; Zammit, 2008). The signal to satellite cells indicating muscle damage is believed to be

a splice variant of insulin-like growth factor-I named MGF (mechanosensitive autocrine

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growth factor), this belief is supported by the finding that levels of MGF peak transiently in

the immediate aftermath of muscle damage by either mechanistic or chemical means (Hill et

al.,2003)

Satellite cells can be identified in both the quiescent and activated state through their

molecular profile (Gnocchi et al., 2009). Quiescent cells have very high Pax7 and Pax3

(paired box transcription factors) expression (Sambasivan et al., 2011). Whereas activated

cells have very upregulated muscle specific expression factors such as: myogenin, MyoD,

Mrf4, and Myf5 (Beauchamp et al., 2000; Dumont et al., 2015).

Unfortunately despite these achievements the scientific community has been unable to

provide a conclusive answer on the issue of satellite cell activation, the ‘molecular switch’

responsible for this conversion has remained elusive (Dhawan & Rando, 2005). In order to

investigate this, a representational difference analysis was performed, this is a hybridisation

based technique which was used to identify unique/highly expressed genes in the quiescent

state cell in comparison to the activated. A milieu of fragments was obtained, one of which

was recognised via BLAST analysis to be transcribed from the hepatic leukaemia factor gene

situated on chromosome 11. This gene is well known to perform a crucial role in muscle

satellite cell differentiation (Rajan et al., 2012). However the fragment appeared to have no

protein coding ability when examined using protein modelling databases. The aim of this

project therefore is to test the hypothesis that satellite stem cell quiescence may be in part

controlled by the query sequence in a non-protein coding regulatory capacity.

A recent study found that only 20% of the transcribed genome results in protein coding

sequences leaving the other 80% (approx. 63% of the entire genome) with either no function

or a non-protein coding regulatory function (He et al., 2015). Non–coding RNA has been

well documented and characterised (snoRNP, miRNA, RMRP, RNAse P, mRNA, tRNA, and

rRNA) and is known to play a vital role in maintenance of healthy genomic function. Despite

this knowledge, only until recently the vast majority of long non-coding RNA (RNA over

200 bases long with no protein coding ability) was believed to be nothing more than a result

of ‘transcriptional noise’ (Guttman et al., 2009; Mercer et al., 2009). Transcribed regions of

the genome with no role whatsoever. However recent studies have suggested that due to its

pervasive nature it may be premature to label it as such (Wagner & Flärdh, 2002). The

FANTOM (functional annotation of the mammalian genome) (St. Laurent et al., 2015)

consortium has used deepCAGE analysis to monitor the dynamics of transcription start sites

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during cellular differentiation, and have come to the conclusion that lncRNA may have as

large a role in regulation as other more well-known RNA subtypes (Chang, 2013; Vance &

Ponting, 2014). This is also highlighted by the role lncRNAs may be playing in cancer

tumorigenesis and tumour differentiation (Eduardo et al., 2004).

LncRNA regulation of the genome is believed to occur by 4 mechanisms (Wang & Chang,

2011). These mechanisms are not intended to be read as being mutually exclusive of each

other and a fully functioning molecule would be predicted to perform a combination of the

roles described here:

Signals. In order for a fully functioning lncRNA to successfully bind to the gene

requiring epigenetic regulation a myriad of transcriptional and developmental cues

must first be met (Ravasi et al., 2006). This extensive control means that only at select

points in the cell cycle will the required conditions synchronise and therefore result in

the lncRNAs transcription and function. Due to this high level of regulation lncRNAs

act as excellent signal molecules to indicate very specific cellular conditions. Xist is

perhaps the best known lncRNA and is associated with the signal (and cis guide)

characterisation of lncRNA function. It plays a vital role in the inactivation of the X-

chromosome through histone modifications such as methylation and acetylation. This

is so as to increase the extent of heterochromatin throughout the structure (Chow et

al., 2005). Xist does this by binding with high affinity to specific sequences in spatial

proximity to sites on the chromosome densely populated with regions of genes being

actively transcribed. From here it spreads in as many directions as permissible until

every available gene is silenced, this would not be possible with as much efficiency

without the lncRNA signalling to the requisite enzymes (Engreitz et al., 2013).

Decoys. LncRNAs may act as decoys or ‘molecular sinks’ to titrate factors which

themselves regulate expression of the genome in a more direct way (transcription

factors and chromatin modifiers especially). This mechanism of lncRNA functions to

simply deny the effector the opportunity to successfully bind and exert the its effect,

upon downregulation of the RNA this then allows near immediate action by the

protein. This relationship shares certain similarities with the switch like activation

observed in cyclin concentrations in the cell cycle, the concentration of the effector

can build up but be supressed by the RNA until the ideal time. Once this suppression

is removed a rapid response is observed due to the already high levels of effector

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present within the nucleus. The lncRNA TERRA is an excellent example of this due

to its ability to inhibit the action of the enzyme telomerase (Redon et al., 2010). It is

actively transcribed from the telomere region of the chromosome and acts as a

template strand to the telomerase enzyme which it binds to with very high affinity.

The lncRNA is even ‘anchored’ to the telomeric 3’ end chromatin to retain the

enzyme in an isolated region whilst inhibiting it. The transcription of this regulatory

RNA is closely associated with stages of the cell cycle, as the cell begins to approach

S phase the TERRA levels are downregulated (Masatoshi et al., 2013). This allows

the enzyme to be released and perform its function of telomeric expansion in

preparation for DNA replication.

Guides. LncRNA may act as a guide to direct complexes of proteins and other RNA

molecules to the correct points so as to have the desired effect. This guidance can

occur either in a cis or trans position relative to the site of lncRNA transcription. Cis

regulation occurs near to the site of transcription and in some cases can be initiated by

co-transcription by RNA polymerase to ensure near immediate control of a gene. Cis

regulation can also be achieved by lncRNA acting as a near target for other epigenetic

regulators. Trans regulation often takes the form of the RNA molecule integrating into

the double helix as a heteroduplex (RNA:DNA) or even as a triplex

(RNA:DNA:DNA) far from its site of transcription. COLDAIR is an example of how

lncRNA also performs important roles in plants, this RNA is transcribed only under

sustained cold conditions (20 days or more) and acts as a guide for PRC2 to modify

FLC (flowering locus C) a potent floral repressor. The repression of this locus allows

the plant to prepare to flower quickly come spring. A process known as vernalisation

which would prove to be much harder in the absence of the regulation provided by the

specific guiding of PRC2 by the RNA (Heo & Sung, 2011).

Scaffold. LncRNA may act as a scaffold for multiple regulators to form a

ribonucleoprotein complex at the site of regulation. In order to successfully perform

this role the molecule must have multiple binding sites and a very high degree of

stability. A very common epigenetic modification made by these lncRNA-RNPs

(lncRNA-RiboNucleoProteins) is directed towards the histone wrapping around a

gene, this can of course result in either negative or positive regulation of transcription.

Pericentric heterochromatin is an extremely important component in normal genome

function (Probst & Almouzni, 2011). Alpha satellite repeat lncRNA is transcribed

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from the site at which this unique patterning of chromatin must be formed, and acts as

a scaffold and direction orientate for the proteins to attach and construct this highly

specialised molecule in the correct way (Wang & Chang, 2011).

As stated above, in the ‘Guides’ section, it is often essential that lncRNA work very closely

with the polycomb repressive complex in order to silence gene expression by post-

translational modification of histones (Margueron & Reinberg, 2011; Moran et al., 2012).

The PRC was first described in Drosophila melanogaster as a repressor of homeotic genes by

Jacobs and Lohuizen (1999), they went on to explain that often this repression was balanced

by a close relationship with trithorax proteins which maintain activated genes. The

combination of these two antagonistic effectors is a crucial factor in the control of cell

proliferation. The complete polycomb repressive complex can be thought of as two separate

complexes, PRC1 and PRC2, working in tandem to trimethylate lysine 27 of histone 3

(otherwise known as H3K27me3) to prevent binding of the essential transcription machinery.

This is due to extremely tight heterochromatin and steric repulsion (Margueron & Reinberg,

2011). This trimethylation is catalysed by an enzyme called the enhancer of Zeste (EZH2)

(Kuzmichev et al., 2002) which concurrently methylates the lysine residue from mono- to di-

to finally a tri- state (this enzyme must also work closely with deacetylases as lysine cannot

be methylated and acetylated simultaneously). Interestingly the PRC has also been associated

with the maintenance of pluripotency through selective gene regulation. This is in order to

preserve the genomic chromatin pattern found in embryonic stem cells (Boyer et al., 2006)

and prevent differentiation of a cell in line with the development of the organism as a whole.

This situation describes precisely the very principle observed in satellite stem cells and as

such deserves a great deal of attention, especially when considered with the fact of the close

association between the PRC and lncRNA acting in cis regulation. The gene being regulated

in this instance is potentially Mmd (monocyte to macrophage differentiation associated

factor), a gene which when transcribed has powerful ability to differentiate a cell and which

in M. musculus is located immediately next to the site of the proposed lncRNA transcription.

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DATA SOURCE AND ANALYTICAL METHODS

RDA (see fig.1) is a

technique used in molecular

biology to amplify

highly/uniquely expressed

transcripts in one

population compared with

another (Lisitsyn & Wigler,

1993). mRNA is taken from

a ‘tester’ and a ‘driver’ cell.

The tester cell containing

the genome of interest and

is converted to cDNA

through reactions with

reverse transcriptase. This

cDNA is then digested using a frequently cutting restriction endonuclease (DnpII was

used in this case) into smaller nucleotide sequences. This digestion allows a linker to

be ligated to both ends of the cDNA molecule. GATC would be the binding site for

the linker following digestion with DnpII. The polymerase chain reaction (PCR) is

then used to generate the initial amplicons of the different genomes. Following this

amplification the linkers of the tester and driver cDNAs are digested. However a new

linker is added to the tester molecules. The tester and driver populations are then

combined in a 1:100 ratio (tester in excess) to promote hybridisation between

sequences common to both the tester (quiescent satellite cell) and driver (activated

satellite cell). Following this hybridisation PCR is again used to exponentially

increase the population of homoduplexes, this is aided by priming sites on the

attached linkers (Tyson et al., 2002). Following this identification of genes

highly/uniquely expressed the sequence can be inserted into a vector and cloned

multiple times. In this instance the sequence was placed into a bacterial plasmid

(pBluescript KS+) which had been digested with BamH1. This restriction

endonuclease was chosen due to BamH1 and DnpII sharing the same restriction site

and therefore creating complimentary ends. The T7 promotor is situated immediately

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Fig. 1. Shows a schematic of the steps involved in the process of representational difference analysis.

upstream of the BamH1 restriction site of pBluescript KS+ and so could be used to

initiate transcription, following this the transcribed molecule can be sequenced using

an automated sequencing analyser.

NCBI BLAST (Basic Local Alignment Search Tool) was used to locate the position

of the sequence on the M. musculus genome. NCBI BLAST also provides information

on the gene from which a query sequence is predicted to originate. This includes

direction and number of transcripts. (http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE

_TYPE=BlastSearch&BLAST_SPEC=OGP__10090__9559&LINK_LOC=blasthome

).

Ensembl was used to double check the location and to use the information graphics

available through this particular online genomic database. Such as the regulatory

functions performed by the gene in which the sequence is predicted to exist, and the

level of conservation of the gene of interest.

(http://www.ensembl.org/Mus_musculus /Location/View?db=core;r=11:90358477-

90358739;tl=Q2bh3bm7lJjEKw3B-1104329-233254412)(Cunningham et al., 2015).

UniProt is a comprehensive database of manually curated and annotated protein

sequences and their functional information. This is possible because entries are taken

from genome sequencing projects. It also contains a large amount of information

about the biological function of proteins as found from the research literature (UniProt

consortium, 2015). UniProt was used to attempt to find any protein encoded for by the

sequence, and the role such a protein may have in the body.

(http://www.uniprot.org/blast/).

Diana lnc-BASE is an online database capable of predicting miRNA recognition

elements (MREs) on mouse and human lncRNA, these predictions are based on

experimentally verified results between miRNA and lncRNA. The database also uses

up to date lncRNA resources such as HITS-CLIP and PAR-CLIP high-throughput

results to provide reliable predictions of RNA protein interactions (Paraskevopoulou

et al., 2013). Diana lnc-BASE was used to establish whether or not there was a

relationship between any experimentally verified miRNAs and the (predicted)

lncRNA sequence. (http://diana.imis.athena-innovation.gr/DianaTools /index.php?

r=lncPredicted/results&keywords=chr11:99965680-

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99965919&genes=&mirnas=&descr=&glocs=chr11:99965680-

99965919%20&threshold=0.7).

The MiRBase is a database of experimentally verified miRNA sequences and

structures. Each result represents a hairpin motif present in a miRNA molecule, as

well as information on genomic location and complete sequence in the mature

molecule (Kozomara & Griffiths-Jones, 2014). MiRBase was used to gain more

information on the miRNA predicted to attach to the query sequence from the Diana

lnc-BASE analysis. (http://www.mirbase. org/cgi-bin/mirna_entry.pl?acc= MI000

5476).

RepeatMasker is a program that analyses DNA sequences for interspersed repeats and

low complexity sequences. The results show a detailed breakdown of the number of

transposable elements (TEs) detected and the percentage of the sequence they take up,

it also shows the percentage GC content which can be used to estimate

uncharacteristically low complexity (Smit et al., 2013-15). RepeatMasker was used

because TEs are common indicators of lncRNA sequences within the genome

(Kapusta et al., 2013). (http://www.repeatmasker.Org

/tmp/36b957eab50f321fbcb47e9f912cc37a.html).

LncRNA2TARGET is a database which contains genes reported to be affected by

lncRNA regulation. The results then show published papers purporting to describe the

effect of either up or down regulation of lncRNAs, and any knock on effect his has on

the function of the gene in question. The database also provides information on any

changes this has on the pathways the products of that gene may be involved in (Jiang

et al., 2015). LncRNA2TARGET was used to research whether the Mmd gene had

been experimentally verified to be affected by lncRNA regulation.

(http://www.lncrna2target.org/search.jsp).

NONCODE is a database which searches for sequence homology between query

sequences and documented non-coding RNA sequences. It differs from other

databases reporting to complete a similar function because of the sheer number of

sequences available for comparison and the variety of noncoding RNAs used (all of

them except tRNAs and rRNAs). The database also presents expression profiles based

on results gained from RNA-seq (Zhao et al., 2015). NONCODE was used to try and

identify an lncRNA with high homology to the query sequence so more could be

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learnt about the processes it is involved in within the body.

(http://www.noncode.org/blast/blast.html).

DeepBASE is a platform for displaying transcription factor binding maps, expression

profiles and transcriptional regulation of ncRNAs such as lncRNA, microRNA,

snoRNAs, tRNAs, snRNAs etc. as well as protein-coding genes from ChIP-Seq data.

ChipBASE is an online platform within deepBASE for decoding transcription factor

binding sites on query sequences, while also providing information on any

transcriptional regulation of non-coding RNA molecules. To provide this sort of

analysis it uses data derived from chip-SEQ experiments of the genome (Yang et al.,

2013). Chip-SEQ is a technique which combine’s chromatin immunoprecipitation

with DNA sequencing to pinpoint the sites of protein interactions on DNA.

ChipBASE was used primarily to see if there was any lncRNA present but also to see

the transcription factors which may work to control the Hlf gene in a post-embryonic

progenitor state. (http://deepbase.sysu.edu.cn/chipbase/).

LnciPedia is a database for human long non-coding RNA (lncRNA) transcripts and

genes. Numerous statistics are generated for each entry such as secondary structure,

protein coding potential and microRNA binding sites (Volders et al., 2013).

LnciPedia was used to research the human equivalent of the Hlf lncRNA described by

earlier resources to try and establish some comparisons.

(http://www.lncipedia.org/db/gene/lnc-HLF-1).

LncRNAdb provides in depth annotation of eukaryotic long non-coding as well as

providing links to recent literature surrounding many of its entries (Amaral et al.,

2011). LncRNAdb was used for the same reason as LnciPedia. (http://www.lncrna

db.org/Kcnq1ot1/).

LnceDB is a database providing information not only on the identity of the non-

coding RNA transcripts but also on the potential for the presence of ‘competing

endogenous’ pairs (Das et al., 2014). These are lncRNA molecules which can regulate

the expression of mRNAs by competing for the microRNAs which may otherwise

prevent translation. (http://gyanxet-beta.com/lncedb/).

mFold webserver hosts software able to use bioinformatics tools to predict the most

energetically favourable folded structure of an RNA molecule (Zuker, 2003). It was

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used to show the most likely secondary structure of the query sequence to allow for

comparison with the hypothesised structure of lncRNAs provided by Novikova et al.

(2012). (http://unafold.rna.albany.edu/?q=node/60).

Royal Holloway, University of London library search and PubMed databases were

also used to access scientific literature surrounding this field of research.

(http://www.ncbi.nlm.nih.gov/pubmed &

http://librarysearch.rhul.ac.uk/primo_library/libweb/action/search.do?

vid=44ROY_VU2&reset_config=true).

RESULTS

The sequence provided had the code: CTGGGTACCGGGCCCCCCCTTTTGGTCGACGGT

ATCGATAAGCTTGATATCGAATTCCTGCAGCCCGGGGGATCGGAGCAAGAATGT

CACGTGCGTGGCTCTTTTCTGGAAGACGAACATGGCTATGGCACGGAGGTGGGG

TTAGCCGAGGACCAGAACGGGTGAGACTCGTTACTGATATGGGATGAGTGTGAG

TTTTAACGTGGATACCTGGATAGGTGTTATCAGTCACTGACAGGGGAGATAGGC

AGAATGAACACATCCAGAGGGAGAGGCCTCTAGTGACCCCATGTGCCCATGGAT

CCACTAGTTCTAGAGCGGTCGCCACCGCGGTGGAGCTCCAATTCGCCCTATAGTG

AGTCGTATTACGCGCGCTCACTGGCCGTCGTTTTACAACGTCGTGACTGGGAAAA

CCCTGGCGTTACCCAACTTAATCGCCTTGCAGCACATCCCCCTTTCGCCAGCTGG

CGTAATAGCCAAGAGGCCCTCACCGATTCGCCCTTCCCAACAGTTGCGCAGCCTG

AATGGCTGAATGGGACTCGCCCTGTAGCGGCGCATTAAGCTGCNGGCNGGNCNT

GGTGGTACTCGCAGCGTGACCGCTACACTTGCCAGCGCCCTANCNCCCGCTCCTT

TTCGCTTTNNTNCCCTNCCCTTCATTCGACNCANTTCACCTGCTTTNCCCGACAAG

CNTCTAAATCNGGNGGCTCACTTTAAGGTTTCGATTTTANNGCTTTNCGGGTCCT

TCGACCCTAAAAANTTTGTCTTTGGTGATGGGTCACGNACGTNGGCACANNCCCN

NAATNNCGNTTTCTCGTCTTTAACGTNCGGAGGNCCACATTT.

It is important to consider that this is the ‘raw’ sequence generated using pBluescript KS+

recombinant technology with a T7 promotor as a primer. This sequence therefore contains

vector sequences upstream and downstream of the insertion site.

Hlf genomic location and protein coding incapability

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Fig. 2. Shows a simplified diagram representing sections of the query sequence as bands of colour depending on their degree of alignment. This diagram for instance

shows that between 50-300 base pairs (approx.) the alignment score is over 200. There is a smaller section of reasonably high homology (score of 80-100) between 460 and 525 (approx.). This was generated through the input of the sequence into the NCBI

BLAST program. 

To begin the project the sequence was inputted to the NCBI BLAST programs M. musculus

database. This seemed the most logical first step to take as it allowed me to pinpoint exactly

where on the genome the sequence was most likely to have been transcribed from (Altschul

et al., 1990). The results predicted that the sequence had a region of high homology with the

Hlf (Hepatocyte leukaemia factor) gene on chromosome 11 of the M. musculus genome, this

is reliably indicated due to the exceptionally low e-value associated with the Hlf gene

prediction (see figure 2). There is also shown to be sequence homology with SLIT3 and

Ccdc46, however these are comparatively small sequences and so this result is almost

certainly down to chance. Figure 3 shows a more in depth analysis of precisely where in the

genome the sequence is believed to have a high homology with and the percentage of gaps in

homology. There is no homology at the beginning or end of the sequence however this is due

to segments of pBluescript KS+ being transcribed unintentionally from the T7 promotor.

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Fig. 4. Shows a graphic detailing the spatial locations and directions of the genes on chromosome 11 of the mouse genome at the 100 million base pair mark. Hlf gene is

shown in a central position residing immediately upstream of the Mmd gene.

Fig. 3. Shows an in depth analysis of the sequence; this gives information on the name of the genes showing high homology as well as showing the respective expected

values. The expected value gives the probability that the similarity is due to chance. Also shown are the location of any gaps in homology and the genomic location of the

predicted genes. This was generated through inputting the sequence into the NCBI BLAST program.

After this the gene was viewed in a graphics outlook (see figure 4) so as to view other

genes/pseudogenes/ near to it and to establish whether it was in a forward or backward facing

direction. The results showed that the Hlf gene is positioned on the reverse orientation and

exactly

next to the powerful Mmd gene.

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Fig. 5. Shows the Ensembl BLAST analysis for the Hlf gene. Clearly shown here are the multiple possible transcripts and the regulatory features bar indicating that the Hlf genes primary role is as an enhancer. This result was generated in an identical way to the NCBI

BLAST results, with the input of the query sequence into the program, however this is performed using the ensembl database.

 

To confirm these results and utilise certain features not available through NCBI the same task

was performed using Ensembl (see figure 5) this showed concurrent predictions that the

sequence was transcribed from the Hlf gene.

It also showed that the gene is highly conserved in mice and that its primary role is to act as a

promoter. Ensembl also showed that Hlf has only two of five of its transcripts made with

protein coding capabilities, the other three are known as processed transcripts and are unable

to be translated to form a functional polypeptide.

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Fig. 6. Shows a function of the database ‘deepBASE’ where it allows a section of genome to be analysed experimentally for verified binding sites. In this case, not surprisingly, both

CTCF_26956 and Essrb_14600 are shown to have binding sites in the Hlf gene, these are both transcription factors shown to have a large effect in stem cell development. This result was

generated through specifying the section of genome on which the Hlf gene is located

DeepBASE was then used for its unique genomic analysis, in so much that it allows users to

select for a number of binding sites of numerous genomic regulators on a gene of interest.

This

showed

that the Hlf gene has binding sites for the stem cell linked transcription factors CTCF and

Esrrb (see figure 6). This result supported the indication from the RDA performed by Dr

Beauchamp that Hlf may be playing an important role in satellite cell quiescence.

To deduce whether or not the sequence belonged to a protein coding transcript or a non-

protein coding transcript the Uniprot program was used as this is able to predict protein

identity from nucleotide sequences. The results showed that the sequence had no inherent

protein coding abilities (see figure 7). This was made clear as despite showing proteins which

can be translated from this sequence, they were only found in organisms other than M.

Musculus such as Piscirickettsia salmonis and various bacteria. Following this result the

investigation was directed to expand on the idea that the sequence given was potentially a

fragment of a larger long non-coding RNA.

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Fig. 7. Shows the UniProt results when trying to deduce which protein this sequence is translated into. As is clear none of these are genuinely potential candidates as they are all

found in species other than M. musculus. This result was generated from the UniProt website through inputting the query sequence

Fig. 8. Shows the potential miRNAs that are predicted to bind to the region of the mouse genome in which the sequence is positioned. These are presented in descending order of

likelihood with a threshold value of 0.7, below which the likelihood is deemed too low to be relevant. This was result was generated from the DIANA TOOLS by specifying the region of

genome associated with the Hlf gene.

Evidence for partnership with miRNA

Following extensive reading a recurring theme in the literature was the partnerships often

found between micro-RNAs and the site of transcription of lncRNAs. This led me to use the

DIANA-lab prediction tool in order to find any micro-RNAs which are able to bind to the

segment of genome from which the query sequence is transcribed (see figure 8).

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Fig. 9. Shows a page from a database of miRNAs detailing miR-883a-5p as having a stem loop structure as well as its genomic location on chromosome X. This database is known as

miRBase and results are easily accessible through a simple name search

Fig. 10. Shows the sixth table available from the paper by Calabrese et al. (2007). It shows the micro-RNA 883a’s reads per library of: J1, J1aza, Dicer+/+, and Dicer-/-

embryonic stem cells in mice, as well as other pieces of information including number of repeat overlaps and conservation in other organisms.

The results showed a predicted miRNA named micro-RNA-883a-5p which had had an 87.2%

probability of being able to bind to the site. Upon further research into this molecule it was

uncovered that it was coded for on chromosome X of M. musculus and had a hairpin motif

structure (see figure 9).

It has also been identified as potentially being amongst a group of miRNAs playing crucial

roles in cell cycle regulation of Dicer+/+ embryonic stem cells in mice (see figure 10).

19

Fig. 11. Shows the results from RepeatMasker indicating that in the extended section of genome surrounding the sequence there is at least two transposable elements. These

are very common markers of lncRNA presence. To get this result the region of genome associated with the Hlf gene was specified.

Transposable elements

Another very common characteristic of lncRNAs are the presence of transposable elements,

at least one is present in 84.3% of all lncRNAs (Kelley & Rinn, 2012), in order to check if

there were any present in the region of the Hlf gene the repeat masker program was used(see

figure 11). This showed that the region does indeed contain transposable elements, one LINE

(long interspersed repeats) named L3/CR1 and one SINE (short interspersed repeats) Alu

element.

Mmd gene analysis

At this point the focus was turned to the Mmd gene in an attempt to incorporate more

knowledge of its sequence and functions to the hypothesis. First of all the gene was viewed in

Ensembl (see figure 12) where it was shown that it had a CTCF transcription factor binding

site, as does Hlf.

20

Fig. 12. Shows the ensembl BLAST analysis for the Mmd gene. Under regulatory features acting on the gene there is a CTCF transcription factor binding site. This also shows many of

the processed transcripts as well showing the Mmd gene to be primarily involved as a promoter gene and enhancer. This is clear from the regulation features legend.

Fig. 13. Shows an informatics of the roles of the Mmd gene and the tissues in the body in which it is highly expressed.

Figure 13 shows an infographic identifying Mmd as being highly expressed in

musculoskeletal systems and also being involved in the biological processes of cell

differentiation and system development. This finding warranted further investigation into

Mmd and any potential links with maintaining quiescence in satellite cells.

21

Fig. 14. Shows the nucleotide sequence for the monocyte to macrophage differentiation factor gene. This shows a segment which is extremely GA rich, a classic sign of a PRE (Polycomb

Response Element) as explained by Chu et al. (2011). This result was generated by getting the FASTA sequence for the Mmd gene from the NCBI website and searching the page for

GAGA repeats.

Fig. 15. Shows the nucleotide sequence for the monocyte to macrophage differentiation factor gene. This shows a second segment which is extremely GA rich,

a classic sign of a PRE (Polycomb Response Element) as explained by Chu et al. (2011). This result was generated by getting the FASTA sequence for the Mmd gene

from the NCBI website and searching the page for GAGA repeats.

To perform this further investigation regulation by the PRC2 was investigated, to do this

PREs (polycomb response elements) were researched and the Mmd gene sequence was

analysed to identify common nucleotide sequences attributed to these elements (see figures

14, 15, and 16). This analysis showed uncharacteristically concentrated bands of nucleotides

at points along the gene concurrent with the sites of PRC2 occupancy.

22

Fig. 16. Shows the nucleotide sequence for the monocyte to macrophage differentiation factor gene. This shows a segment which is extremely rich in GTGT

motifs, another reliable indicator of PREs as explained by Okulski et al. (2011). This result was generated by getting the FASTA sequence for the Mmd gene from the

NCBI website and searching the page for GTGT repeats.

Fig. 17. Shows the result of searching for the Mmd gene in lncRNA-Target, this brought up two papers where the knockdown of lncRNAs had an effect on the

expression of Mmd. This result was generated through searching the Lnc-RNA –Target database with the Mmd gene entrez number.

To identify any previously confirmed relationships between Mmd and lncRNA lncRNA-

target was used (see figure 17). This showed 2 papers which between them had identified 8

lncRNAs which when overexpressed/knocked down altered the expression of the Mmd gene.

This result showed that in the mouse there are proven interactions between lncRNAs and the

Mmd gene.

23

Evidence for Hlf- lncRNA in mice and HLF- lncRNA in humans

After this the lncRNA sequence was compared with that of other non-coding RNAs in hope

of uncovering a significant homology with a more experimentally researched lncRNA by

using non-code (see figure 18).

24

Fig. 18. Shows the results from the non-code database analysis of the sequence. This shows that the probability of the sequence sharing alignment with other non-coding

RNAs purely through chance is very low, this is indicated by the expect values having magnitudes of the order e-67. This result was obtained by searching the database with

the query sequence. Unfortunately the non-code database had some technical issues

which prohibited taking these results any further. Following this disappointment the mFold

webserver was used to visually assess any similarity between the secondary structure of the

query sequence (minus the first and last 20 nucleotides due to 800 nucleotide limit, these

nucleotides were chosen due to their likely source being pBluescript KS+) and the

hypothesised sequence shown by Novikova et al. (2012) (see figure 19).

After this a search was performed on LnciPedia (see figure 20), this showed an lncRNA

present in humans which is also transcribed from the HLF gene. It also showed that its locus

of transcription is conserved in mice. Following this result genes near to HLF in the human

genome were checked to see if any of these had roles in cell differentiation processes. The

results from NCBI show that the HLF gene is again immediately next to the MMD gene (see

figure 21). When considering that lncRNAs are known to act in cis (locally) on cell

differentiation factors the conservation of this genomic locus in both the mouse and human

lineage is highly relevant, and implies support for the stated hypothesis.

25

Fig. 19. This shows the similarity in the query sequence predicted RNA folding and the hypothesised folding of lncRNA by Novikova et al. (2012) shown on the right

hand side. In particular the presence of extended stem-loop structures. The predicted folding of the query sequence was generated by inputting the sequence into the mFold

webserver provided by the RNA institute of the University at Albany.

Fig. 20. Shows a result from the LNCipedia database in which a known and documented lncRNA is transcribed from the HLF gene on chromosome 17 of the human genome. This

result was obtained by searching the database for the keyword Hlf.

26

Fig. 21. Shows the HLF gene, from which the Lnc-Hlf RNA is known to be transcribed, again located immediately next a gene coding for MMD. This resulted was generated in the same

way as the graphic detailing the HLF position in M. musculus in figure 4.

DISCUSSION

The ability of a satellite cell to maintain its quiescence until required is absolutely vital to

both normal muscle homeostasis and to regeneration following more severe damage

(Gnocchi, 2008). If this ability was to be compromised in an organism then the niche would

soon become exhausted, thus leading to the dystrophy like symptoms of muscular atrophy

and chronic fatigue (Shi & Garry, 2006). As correct muscle function is therefore vital to the

health of an individual it is essential that a comprehensive understanding of quiescence is

developed. Investigation into genes highly/uniquely expressed in the quiescent state is the

most logical first step in achieving this.

Proposed modes of action

In the introduction of this project the hypothesis was stated that satellite stem cell quiescence

may be in part controlled by the query sequence in a non-protein coding regulatory capacity.

The results indicate three possible modes of action for this to be the case: that the query

sequence is a fragment of a larger lncRNA molecule which is working alongside micro-

RNA-883a-5p to form a ribonuclease complex, and downregulate the Mmd gene to prevent

premature cellular differentiation; that the lncRNA is acting as a competing endogenous

partner to the miRNA which may be promoting expression of the Mmd gene; or perhaps a

27

combination of the lncRNA simultaneously interacting with the Mmd gene, while titrating the

miRNA to lower levels. In response to a stimulus indicating a need for new myonuclei this

regulation would be removed through silencing of transcription of the Hlf lncRNA. This

would result in the Mmd gene being able to play a role in the process of differentiation

required to convert a quiescent satellite cell to a mature myoblast. Due to the spatial

proximity of the genes involved it is sensible to suggest that any lncRNA action would occur

in a cis fashion, that is to say as either a: scaffold, decoy, signal or guide.

Evidence for proposed lncRNA identity and suggested relationship with miRNA-883a-

5p

The primary evidence for the query sequences proposed lncRNA identity is not only that it

shows a complete inability to code for murine proteins, but also that it shows a high

homology with many other molecules known to have roles in the nucleus in a non-protein-

coding capacity. When this is considered alongside results showing that it has a very similar

secondary structure to predicted lncRNA secondary structure and that there are high

probabilities that it binds to micro-RNAs known to be highly expressed in stem cells, it

creates a compelling argument. MicroRNAs are defined as a subtype of short non-coding

RNA molecules that post-transcriptionally regulate the expression of protein-coding genes

through imperfect base pairing with the 30-UTR of target mRNAs (Bartel, 2004). The

expression of miR-883a-5p in embryonic stem cells alone may not provide conclusive

evidence of its relevance to this model. However, when taken alongside the fact that it had

the highest prediction of any microRNA to attach to the Hlf gene lncRNA site, a site which

could potentially provide a crucial role in a cell maintaining a progenitor state, this becomes

increasingly relevant. There is also the possibility that the two are working antagonistically as

competing endogenous pairs, this would be a very probable model if miR-883a-5p was

proved to be essential to the expression of Mmd.

Maintenance of progenitor cell quiescence and the role of CTCF in this model

Maintenance of a progenitor cells quiescent state is believed to be composed of two elements;

the maintenance of pluripotent programs and the repression of differentiation programs.

Guttman et al. (2011) compared the expression profiles of differentiated cells with the

expression profiles of cells in which predicted lncRNAs had been ‘knocked down’, these bore

a striking resemblance. This led them to conclude that lncRNAs must be acting at least in part

as barriers to terminal cellular differentiation. Somewhat confusingly the observed cells

28

showed all the lineage specific markers indicative of a committed cell but remained

undifferentiated. This is apparently a fairly common phenomenon and can also be observed in

cells in which several chromatin regulators are knocked down. They also discovered, through

the use of silencing shRNAs, that the differentiation associated lncRNAs had binding sites for

pluripotent associated transcription factors such as OCT4 and Nanog. CTCF is similar to

Oct4 and Nanog in that it is a transcription factor intimately involved with the differentiation

of many cell types throughout the body. The importance of CTCF cannot be underestimated

as fluctuating levels of expression in cells can lead to changes in key genes

inactivation/repression, they state that this could result in either incomplete or premature

differentiation (Plasschaert et al., 2014). Koesters et al. (2007) identified CTCF as playing a

crucial role in many of the processes maintaining the progenitor character of monocytic cells

and observed that expression levels were downregulated during lineage commitment to

human dendritic cells. The CTCF zinc finger transcription endonuclease is already known to

be involved in regulation of myogenic differentiation processes via control of lineage-specific

genes including: Myc, Pax7 and MyoD (Delgado-Olguín et al., 2011). It is therefore

significant that it also has binding sites on the genes Hlf and Mmd, as CTCF may be

interacting with the Hlf gene to promote transcription of the lncRNA in order to enhance

repression of Mmd or other differentiation associated genes. CTCF is also known to perform

roles in the regulation of the chromatin interactome (Handoko et al., 2011), because of this it

may be working alongside the lncRNA (acting in a guide-like capacity) to increase the

percentage of heterochromatin surrounding the Mmd gene. The overall process may even be a

combination of the two, such is the diversity of the CTCF molecule.

Control of MMD by lncRNA: miRNA partnerships and presence of PREs within the

gene sequence

The biological process of monocyte to macrophage differentiation is a very well-studied

example of cellular differentiation and acts as an excellent template for the proposed mode of

cellular activation following loss of satellite cell quiescence. The Mmd gene itself is shown to

have a high expression in muscle cells and is of course vital in many cellular differentiation

processes, not only the example provided below. In the conversion of the self-renewing

hematopoietic stem cells the MMD gene is controlled by an lncRNA (Lnc-MC) and a micro

RNA (micro-RNA-199a-5p) as has already been described by Chen et al. (2015). In this

example the lncRNA acts as both a repressor and as a promoter of the MMD gene through

chromatin remodelling and sequestration of silencing miRNAs respectively. In non-small

29

lung cancer however another micro RNA (miR-140-5p) acts as a repressor of MMD gene

activity and is therefore being pursued as a potential candidate for drug development (Li &

He, 2014). The contrasting roles of these microRNAs serve to show that they are capable of

both repression and promotion. Unfortunately no such study has been made on the effect of

miRNA-883a-5p on Mmd and so it is unclear whether to maintain a cells quiescence its

concentrations should be maintained or silenced. The characteristic signs of PREs situated

throughout the Mmd gene found in M. musculus strengthens the argument that there is a

degree of gene regulation involving chromatin modification through PRC2 action, and not

solely through miRNA sequestration by an lncRNA. Kelley and Rinn (2012) also state

lncRNAs are preferentially situated in close proximity to the developmental regulators upon

which they act. By this definition Mmd would appear to be an ideal site to be situated next for

any lncRNA involved in the continuation of the quiescent satellite cell state. The fact that the

locus in which the human HLF lncRNA and the MMD gene are transcribed from is conserved

implies that there may be an evolutionary advantage to keeping them close together. A

sentiment similarly expressed by Kapusta et al. (2013) when they propose that some

lncRNAs loci may be traceable back to ancestors of not only all mammals but of all

vertebrates.

Muscle differentiation and lncRNA

The relationship between lncRNA based regulation and muscle differentiation is also already

well established by Cesana et al. (2011). In this paper they state that linc-MD1 acts as a

competing endogenous factor to miR-133 and miR-135 to regulate the expression of MAML1

and MEF2C (Transcription factors that activate muscle-specific gene expression and advance

the cell towards lineage commitment). They also showed that this control linc-MD1 has an

effect on differentiation timing in human myoblasts and that in Duchenne muscle cells levels

of this are severely reduced. They go on to conclude that linc-MD1 must be acting as a

competing endogenous partner to these microRNAs and that this is a vital component of

healthy muscular development. With this documented example displaying that satellite cells

are already in part governed by non-coding RNA it is entirely plausible to assume that

lncRNA may be acting on various points in the genome to the same extent.

Transposable elements and lncRNA secondary structure

The presence of transposable elements are also a near vital signature of lncRNA presence.

This stems from the non-mutually exclusive theories for the emergence of lncRNAs: (1)

30

emergence from transposable element (TE) sequences; (2) duplication of another lncRNA;

(3) mutation of a protein-coding genes de novo (4) origin from sequences previously

unexpressed or devoid of exonic sequences (Kapusta et al., 2013). The presence of more than

one transposable element is also a positive indicator of lncRNA identity. This is because in

order to reach their correct secondary structure as stem loop molecules the sequences will

usually contain transposable elements running inversely complimentary to each other,

following ‘A to I editing’ the RNA is able to form the necessary intra-molecular bonds

(Nigita et al., 2015).

Further laboratory experiments

There are a number of laboratory techniques available which could be used to further study

whether or not the query sequence is in fact an lncRNA, as well as establishing if it is

downregulating Mmd up until the point of cellular activation (Yan et al., 2012). The first step

would be to perform an RDA to analyse whether or not Mmd expression was altered during

the activation of a satellite cell. This experiment would involve using the cDNA produced

from the activated cell as the tester instead of the driver. For the proposed mode of action to

be correct there should be a large increase in Mmd activity in the activated cell in comparison

to the quiescent.

The second step would be to clarify whether or not the sequence is acting with the PRC2

machinery by using PAR-CLIP (photo-activatable ribonucleoside enhanced CLIP) (Scheibe

et al., 2012). PAR-CLIP analyses the RNA-protein interactome by introducing covalent

bonds between the RNA molecule and protein when the cell is exposed to UV light at

approximately 365nm; it is able to do this because prior to this high dose of energy highly

reactive nucleosides (4-thiouridine) are placed within the cell, these are taken up by the DNA

and more importantly the RNA (Kloetgen et al., 2015). Upon ‘activation’ by the UV light

these analogues form covalent bonds with whichever protein machinery they happen to be in

contact with at the time, if the hypothesis is correct then in the quiescent cell the lncRNA

should be bound to the PRC2 molecule at this time. Following this the cell is broken apart

using lysate buffer and centrifuged. Anti-PRC2 antibodies are then conjugated to magnetic

beads to allow for immunoprecipitation, the supernatant from the centrifuge is then added to

the solution of antibody conjugated magnetic beads where the PRC2 is bound to very

specifically and strongly. Upon introduction of a magnetic field and removal of the

supernatant at this point all non-specific proteins and nucleotides will be removed while the

31

PRC2 (and covalently attached lncRNA) will remain bound to the secured magnetic beads.

At this point the PRC2-lncRNA complex can be radiolabelled using radioactive ATP and

kinase buffer and eluted using the elution buffer to remove it from the antibodies. SDS-

PAGE is the next step where complexes with the correct predicted size can be selected for,

these show up in an agarose gel due to their radioactive nature, thee samples can then be

electroeluted and digested with proteinase to remove the PRC2 and leave only the myriad of

bound RNA molecules.

From these a cDNA library can be made using reverse transcriptase which can then be

amplified using PCR, this would leave a large quantity of stable DNA molecules homologous

to all the RNAs binding to PRC2 proteins in the quiescent satellite cell. After this step next-

generation DNA sequencing would be utilised and following this BLAST to compare the

sequences gained with the original query sequence to ascertain if there is any examples of a

high homology. If this wielded a positive result more specific steps to check for chromatin

signatures of PRC2 occupancy on the Mmd gene itself using CHIRP-SEQ (chromatin

isolation by RNA purification) technology would be taken. CHIRP-SEQ uses the same

crosslinking techniques described above to covalently attach the RBP (ribonucleotide binding

protein), RNA and DNA. Biotinylated oligo-nucleotide tiles, short complimentary nucleotide

sequences able to ‘tile’ the length of an lncRNA molecule, are then added to provide specific

binding to the predicted lncRNA sequence (gained from experiment 1). This cell content

solution is then washed to remove any non-specific cell debris, the oligonucleotides will be

retained due to a magnetic field ensuring the streptavidin is attracted enough to resist being

removed in the supernatant. After this RNAse enzymes are used to degrade any RNA in

solution while proteases are used to degrade any proteins, this leaves only the DNA to which

the PRC2 and the lncRNA have been found. Imitating the final stages of the first experiment

where the sequence is amplified using PCR and then sequenced would result in a definitive

answer as to whether or not a complex of lncRNA transcribed from the Hlf gene is working

alongside PRC2 to silence expression of Mmd and maintain the satellite cell in a quiescent

state.

ACKNOWLEDGEMENTS

I would like to thank Dr Chen at the Chinese Academy of Medical Sciences and Peking

Union Medical College, for allowing me access to his paper free of charge merely a month

32

after its publication. I would also like to thank Dr Beauchamp for his continued support and

guidance throughout the research and writing of this paper.

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Date Length of time

Purpose

06/06/2015 3 Literature research07/06/2015 2 Literature research08/06/2015 3 Literature research09/06/2015 2 Literature research10/06/2015 3 Literature research11/06/2015 2 Literature research12/06/2015 1 Literature research13/06/2015 3 Literature research14/06/2015 2 Literature research30/06/2015 3 Literature research01/07/2015 2 Literature research02/07/2015 3 Literature research03/07/2015 3 Literature research16/07/2015 2 Literature research17/07/2015 3 Literature research19/07/2015 1 Literature research20/07/2015 3 Literature research21/07/2015 3 Literature research22/07/2015 2 Literature research23/07/2015 3 Literature research24/07/2015 1 Literature research25/07/2015 2 Database and modelling systems analysis26/07/2015 3 Database and modelling systems analysis27/07/2015 2 Database and modelling systems analysis28/07/2015 2 Database and modelling systems analysis30/07/2015 3 Database and modelling systems analysis31/07/2015 1 Database and modelling systems analysis01/08/2015 3 Database and modelling systems analysis02/08/2015 3 Database and modelling systems analysis03/08/2015 3 Database and modelling systems analysis04/08/2015 2 Database and modelling systems analysis05/08/2015 1 Database and modelling systems analysis06/08/2015 3 Database and modelling systems analysis07/08/2015 3 Database and modelling systems analysis08/08/2015 1 Database and modelling systems analysis10/08/2015 3 Database and modelling systems analysis11/08/2015 1 Database and modelling systems analysis12/08/2015 1 Database and modelling systems analysis13/08/2015 3 Database and modelling systems analysis14/08/2015 2 Database and modelling systems analysis15/08/2015 3 Database and modelling systems analysis16/08/2015 3 Database and modelling systems analysis17/08/2015 2 Database and modelling systems analysis18/08/2015 3 Database and modelling systems analysis

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19/08/2015 1 Database and modelling systems analysis20/08/2015 3 Database and modelling systems analysis21/08/2015 3 Database and modelling systems analysis22/08/2015 1 Database and modelling systems analysis24/08/2015 1 Database and modelling systems analysis25/08/2015 3 Database and modelling systems analysis26/08/2015 3 Database and modelling systems analysis27/08/2015 1 Database and modelling systems analysis28/08/2015 3 Database and modelling systems analysis29/08/2015 3 Database and modelling systems analysis30/08/2015 1 Database and modelling systems analysis31/08/2015 3 Database and modelling systems analysis01/09/2015 3 Database and modelling systems analysis02/09/2015 2 Database and modelling systems analysis03/09/2015 3 Database and modelling systems analysis04/09/2015 3 Database and modelling systems analysis05/09/2015 1 Database and modelling systems analysis06/09/2015 3 Database and modelling systems analysis07/09/2015 3 Database and modelling systems analysis08/09/2015 1 Database and modelling systems analysis09/09/2015 3 Database and modelling systems analysis10/09/2015 1 Database and modelling systems analysis11/09/2015 3 Database and modelling systems analysis12/09/2015 1 Database and modelling systems analysis13/09/2015 3 Database and modelling systems analysis14/09/2015 2 Database and modelling systems analysis15/09/2015 3 Database and modelling systems analysis16/09/2015 2 Database and modelling systems analysis17/09/2015 3 Database and modelling systems analysis18/09/2015 2 Database and modelling systems analysis19/09/2015 3 Database and modelling systems analysis20/09/2015 3 Database and modelling systems analysis21/09/2015 2 Database and modelling systems analysis22/09/2015 3 Database and modelling systems analysis23/09/2015 1 Database and modelling systems analysis24/09/2015 3 Database and modelling systems analysis26/09/2015 3 Database and modelling systems analysis27/09/2015 3 Project report writing28/09/2015 3 Project report writing29/09/2015 1 Project report writing30/09/2015 1 Project report writing01/10/2015 3 Project report writing02/10/2015 3 Project report writing03/10/2015 2 Project report writing04/10/2015 3 Project report writing05/10/2015 3 Project report writing

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06/10/2015 3 Project report writing07/10/2015 1 Project report writing08/10/2015 3 Project report writing09/10/2015 3 Project report writing10/10/2015 3 Project report writing11/10/2015 2 Project report writing12/10/2015 3 Project report writing13/10/2015 3 Project report writing14/10/2015 3 Project report writing15/10/2015 2 Project report writing16/10/2015 3 Project report writing18/10/2015 3 Project report writing19/10/2015 2 Project report writing20/10/2015 3 Project report writing21/10/2015 3 Project report writing22/10/2015 3 Project report writing23/10/2015 2 Project report writing24/10/2015 3 Project report writing25/10/2015 3 Project report writing26/10/2015 3 Project report writing27/10/2015 2 Project report writing28/10/2015 3 Project report writing29/10/2015 3 Project report writing30/10/2015 3 Project report writing31/10/2015 2 Project report writing02/11/2015 3 Project report writing03/11/2015 3 Project report writing04/11/2015 1 Project report writing05/11/2015 3 Project report writing06/11/2015 3 Project report writing07/11/2015 3 Project report writing08/11/2015 2 Project report writing09/11/2015 3 Project report writing10/11/2015 2 Project report writing11/11/2015 3 Project report writing12/11/2015 3 Project report writing13/11/2015 3 Project report writing21/11/2015 3 Project report writing22/11/2015 1 Project report writing23/11/2015 3 Project report writing24/11/2015 3 Project report writing25/11/2015 2 Project report writing26/11/2015 3 Project report writing27/11/2015 3 Project report writing28/11/2015 2 Project report writing29/11/2015 3 Project report writing

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01/12/2015 3 Project report writing02/12/2015 2 Project report writing03/12/2015 3 Project report writing04/12/2015 3 Project report writing08/12/2015 2 Project report writing

346 hours

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