School of Biological Sciences BS3010 INDIVIDUAL 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 : [email protected]School of Biological Sciences, Royal Holloway, University of London, Egham, Surrey TW20 0EX 1 2015- 8230 X
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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.
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.
15
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.
16
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.
17
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).
18
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).
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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.
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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).
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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
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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
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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.
REFERENCES
Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. 1990, "Basic local
alignment search tool", Journal of Molecular Biology, vol. 215, no. 3, pp. 403-410.
J., Hudson, T.J. & Sladek, R. 2012, "Analysis of early C2C12 myogenesis identifies
stably and differentially expressed transcriptional regulators whose knock-down inhibits
myoblast differentiation", Physiological genomics, vol. 44, no. 2, pp. 183-197.
Ravasi, T., Suzuki, H., Pang, K.C., Katayama, S., Furuno, M., Okunishi, R., Fukuda, S., Ru,
K., Frith, M.C., Gongora, M.M., Grimmond, S.M., Hume, D.A., Hayashizaki, Y. &
Mattick, J.S. 2006, "Experimental validation of the regulated expression of large
numbers of non- coding RNAs from the mouse genome", Genome research, vol. 16, no.
1, pp. 11.
Redon, S., Reichenbach, P. & Lingner, J. 2010, "The non-coding RNA TERRA is a natural
ligand and direct inhibitor of human telomerase", Nucleic acids research, vol. 38, no. 17,
pp. 5797-5806.
Sambasivan, R., Yao, R., Kissenpfennig, A., Van Wittenberghe, L., Paldi, A., Gayraud-
Morel, B., Guenou, H., Malissen, B., Tajbakhsh, S. & Galy, A. 2011, "Pax7- expressing
satellite cells are indispensable for adult skeletal muscle regeneration", Development
(Cambridge, England), vol. 138, no. 17, pp. 3647.
Scheibe, M., Butter, F., Hafner, M., Tuschl, T. & Mann, M. 2012, "Quantitative mass
spectrometry and PAR- CLIP to identify RNA-protein interactions", Nucleic acids
research, vol. 40, no. 19, pp. 9897-9902.
39
Seale, P. & Rudnicki, M.A. 2000, "A New Look at the Origin, Function, and “ Stem- Cell”
Status of Muscle Satellite Cells", Developmental biology, vol. 218, no. 2, pp. 115-124.
Shi, X. & Garry, D.J. 2006, "Muscle stem cells in development, regeneration, and
disease", Genes & development, vol. 20, no. 13, pp. 1692-1708
Smit, AFA, Hubley, R & Green, P. RepeatMasker Open-4.0. 2013-2015
<http://www.repeatmasker.org>.
St. Laurent, G., Wahlestedt, C. & Kapranov, P. 2015, "The Landscape of long noncoding
RNA classification", Trends in Genetics, vol. 31, no. 5, pp. 239-251.
Studitsky, A.N. 1964, "FREE AUTO- AND HOMOGRAFTS OF MUSCLE TISSUE IN
EXPERIMENTS ON ANIMALS", Annals of the New York Academy of Sciences, vol.
120, no. 1, pp. 789-801.
Tyson, K.L., Weissberg, P.L. & Shanahan, C.M. 2002, "Heterogeneity of gene expression in
human atheroma unmasked using cDNA representational difference analysis",
Physiological genomics, vol. 9, no. 2, pp. 121-130.
UniProt Consortium 2015, "UniProt: a hub for protein information", Nucleic acids research,
vol. 43, no. Database issue, pp. D204-12.
Vance, K.W. & Ponting, C.P. 2014, "Transcriptional regulatory functions of nuclear long
non- coding RNAs", Trends in Genetics, .
Verdijk, L.B. 2014, "Satellite cell activation as a critical step in skeletal muscle plasticity",
Experimental physiology, vol. 99, no. 11, pp. 1449-1450.
Volders, P.J., Helsens, K., Wang, X., Menten, B., Martens, L., Gevaert, K., Vandesompele, J.
& Mestdagh, P. 2013, "LNCipedia: a database for annotated human lncRNA transcript
sequences and structures", Nucleic acids research, vol. 41, no. Database issue, pp. D246-
51.
Wagner, E.G. & Flärdh, K. 2002, "Antisense RNAs everywhere?", Trends in genetics : TIG,
vol. 18, no. 5, pp. 223.
40
Wang, K. & Chang, H. 2011, "Molecular Mechanisms of Long Noncoding RNAs",
Molecular cell, vol. 43, no. 6, pp. 904-914.
Yan, B., Wang, Z.H. & Guo, J.T. 2012, "The research strategies for probing the function of
long noncoding RNAs", Genomics, vol. 99, no. 2, pp. 76-80.
Yang, J.H., Li, J.H., Jiang, S., Zhou, H. & Qu, L.H. 2013, "ChIPBase: a database for
decoding the transcriptional regulation of long non-coding RNA and microRNA genes
from ChIP-Seq data", Nucleic acids research, vol. 41, no. Database issue, pp. D177-87.
Yoshida, N., Yoshida, S., Koishi, K., Masuda, K. & Nabeshima, Y. 1998, "Cell heterogeneity
upon myogenic differentiation: down-regulation of MyoD and Myf-5 generates ' reserve
cells'", Journal of cell science; J.Cell Sci., vol. 111, pp. 769-779.
Yu, X.W. & Rudnicki, M.A. 2011, "Satellite cells, the engines of muscle repair", Nature
Reviews Molecular Cell Biology, vol. 13, no. 2, pp. 127.
Zammit, P. 2008, "All muscle satellite cells are equal, but are some more equal than others?",
vol. 121, pp. 2975-2982.
Zhao, Y., Li, H., Fang, S., Kang, Y., Wu, W., Hao, Y., Li, Z., Bu, D., Sun, N., Zhang, M.Q.
& Chen, R. 2015, "NONCODE 2016: an informative and valuable data source of long
non-coding RNAs", Nucleic acids research, .
Zuker, M. 2003, "Mfold web server for nucleic acid folding and hybridization prediction",
Nucleic acids research, vol. 31, no. 13, pp. 3406-3415.
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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