iDarwin AS 0022 (2022 AD)

Volume 2

Original research article
iDarwin volume 2, pages 3-33
Published on May 1, AS 0022 (2022 AD)
Primate deep conserved noncoding sequences and non-coding RNA:
their possible relatedness to Central Nervous System

Nilmini Hettiarachchi1, 2
1Bioinformatics Institute and 2School of Biological Sciences

University of Auckland, 3a Symonds Street, Auckland Central 1010, New Zealand
Email: nilminihett@gmail.com; n.hettiarachchi@auckland.ac.nz

Abstract
Background Conserved Noncoding Sequences (CNSs) are extensively studied for their regulatory properties and functional
importance to organisms. Many features such as location, proximity to the likely target gene, lineage speci city, functionality oflikely target genes and nucleotide composition of these sequences have been investigated, thus have provided very meaningful
insight to signify underlying evolutionary importance of these sequences. Also thorough investigation around how to assign
function to noncoding regions of eukaryote genomes is another area that is studied. On one hand evolutionary analyses, includingfi

3

iDarwin AS 0022 (2022 AD)

Volume 2

signatures of selection or conservation which can indicate the presence of constraint, suggest that sequences that are evolving nonneutrally are candidates for functionality. On the other hand evidence that is based on experimental pro ling of transcription,
methylation, histone modi cations and chromatin state also solidify the functional importance of CNSs. While these types of data
are very important and are associated with function in most cases, this is not always the case. Evolutionary conservation of
noncoding elements that are identi able in more than one species, is still being used as the initial guideline in investigating
function via experiments. As there may be patterns that are often speci cally associated with potentially functional elements,
fully understanding the experimental pro les of conserved noncoding regions may help us construed functionality of conserved
noncoding regions easily.
Results In an effort to integrate experimental pro le data, we investigated evidence of expression of conserved noncoding
sequences (CNSs). For CNSs from ten primates, we assessed transcription, histone modi cations, level of evolutionary constraint
or accelerated evolution, possible target genes, tissue expression pro les of likely target genes and clustering patterns. In total we
found 153,475 CNSs conserved across all ten primates. Of these 59,870 were overlapping non coding regions of ncRNA genes.
H3K4Me1 marks (often associated with active enhancers) were highly correlated with CNSs whereas H4K20Me1 (linked to, e.g.
DNA damage repair) had high correlation with conserved ncRNA regions (ncRNA-gene-CEs). Both CNSs and conserved
ncRNA showed evidence of being under purifying selection. The CNSs in our dataset overall exhibited lower allele frequencies,
consistent with higher levels of evolutionary constraint. We also found CNSs and ncRNA-gene-CEs produce mutually exclusive
groups. The analyses also suggest that both types of conserved elements have undergone accelerated evolution, which we
speculate may indicate changes in regulatory requirements following divergence events. Finally, we nd that likely target genes
for hominoidae speci c, primate and mammalian common CNSs and ncRNA-gene-CEs are predominantly associated with brainrelated function in humans.
Conclusion The deep conserved primate CNSs and ncRNA gene-CEs signify functional importance suggesting ongoingrecruitment of these sequences into brain-related functions, consistent with King and Wilson’s hypothesis that regulatory changes
may account for rapid changes in phenotype among primates.
Keywords: CNSs, ncRNA, histone modi cation, accelerated evolution, purifying selection, brain evolution

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

4

iDarwin AS 0022 (2022 AD)

Volume 2

Introduction
Conservation in the non-coding regions of genomes are elaborately studied over more than a decade and still it keeps revealing
intriguing information about the non-coding regions of genomes, which we once considered as “junk” DNA. It is very clear now
that once referred to as “junk” shapes the lineage and species individuality.
Assessing the functional potential of non-protein coding regions of genomes is a tasking process. Two broad approaches
focus on either evolutionary signals of conservation and non-neutrality (Sandelin et al. 2004; Woolfe et al. 2005; Vavouri et al.
2007; Elgar 2009; Lee et al. 2011; Matsunami and Saitou 2012; Takahashi and Saitou 2012; Hettiarachchi et al. 2014;
Hettiarachchi et al. 2016; Babarinde 2021) or ‘activity’ signals, such as expression, epigenetic or chromatin states. These
approaches have led to strikingly different conclusions regarding the extent of the genome which is functional. Evolutionary
analyses estimate that ~10% of the genome is under constraint, whereas biochemical activities have been attributed to ~80% of
the human genome (The encode project consortium 2012). This has led to major debate regarding the identi cation of biological
function (Graur et al. 2013). On one hand, it is clear that evolutionary analyses may be conservative, in that they frequently rely
on conservation of an element in more than one species in order to predict function. Thus, they may miss species-speci celements or activities. In contrast, a dif culty with assessing functional regions through measurement of biochemical activities isthat it is not clear how to separate bona de functional activities from background non-functional regions. While clearly
conservation reliant computational approach and biochemical measurements are both useful in identifying functional elements,
the debate that followed the publication of the ENCODE project (The ENCODE Project Consortium 2012) suggests that a
cautious approach is required to extend evolutionary analyses to include biochemical activity data (Graur et al. 2013; Doolittle2013). In an attempt to understand whether biochemical activity data relate to evolutionarily derived functional annotation, we
here integrate some of these data into an analysis of conserved noncoding elements from primates. We initially used annotation
data to divide these conserved noncoding sequences into CNSs and ncRNA and downstream use expression data on thesesequences and nd that distinct types of histone modi cation are associated with CNSs and conserved ncRNAs. For CNSs, it isthought that they may act on adjacent protein-coding genes (Woolfe et al. 2005; Elgar 2009; Matsunami and Saitou 2012),
whereas the trans-acting nature of ncRNAs suggests that need not be the case for this class of noncoding elements. We
nevertheless applied this criterion, as some enhancers show evidence of transcription (Andersson et al. 2014; Melamed et al.
2016; Tippens et al. 2018), blurring the lines between ncRNAs and cis-acting regulatory elements. Further Awan et al. (2017)

fi

fi

fi

fi

fi

fi

5

iDarwin AS 0022 (2022 AD)

Volume 2

have found that primate speci c LongncRNAs (lncRNAs) and microRNAs(miRNAs) are two important RNA classes
with regulatory functions while Saber et al.
(2016) have investigated the importance ofhighly conserved coding and noncoding
regions in higher order primates.
In this study we have extensively
assessed and analysed primate common
mammalian common and Hominoidae
speci c, CNSs and ncRNA sequences withrespect to their functional signi cancethrough histone modi cations, genomic
location, target gene expression patterns andclustering patterns. It is vital that weunderstand the evolutionary importance anddynamics of conserved noncoding regions inhumans and our closest relatives. The
functional importance of these regions areworth investigating further as most of theseregions are vital for proper functioning of the
primate genomes. Therefore our motivation
behind this study solely lies in producing avery thorough set of conserved noncoding
sequences that can be at a later stage
experimentally veri ed for its vital

Figure 1. Phylogenetic relationships between the primate species used in the study.
(MYA – Million Years Ago). The data for these species were downloaded from Ensembl
release 94. The divergence times are according to Goodman et al. (1998) and Glazko and Nei(2003).

fi

fi

fi

fi

fi

6

iDarwin AS 0022 (2022 AD)

Volume 2functionality in primates. While we would be hesitant to assign function based on the non-evolutionary components of ouranalyses (histone modi cation, genomic location), these may nevertheless be helpful parameters in determining the function of
genomic elements where comparative data are absent.

Materials and Methods
Genomes used in the analysis
Primate repeat masked genomes of Human (Homo sapiens), gorilla (Gorilla gorilla gorilla), orangutan (Pongo abelii), gibbon(Nomascus leucogenys), rhesus macaque (Macaca mulatta), crab eating macaque (Macaca fascicularis), baboon (Papio anubis),
vervet (Chlorocebus sabaeus), marmoset (Callithrix jacchus), mouse lemur (Microcebus murinus) were downloaded from
Ensembl release 94 (Yates et al. 2020). All genomes used were above minimum of 5X coverage. Figure 1 depicts thephylogenetic relationships between the species used in the study. The divergence times used for the work are according toGoodman et al. 1998; Glazko and Nei 2003.
Masking coding regions
CDS coordinates were downloaded for respective genomes from Ensembl BioMart release 94 (Yates et al. 2020). The CDS
coordinates of different transcripts were merged to remove overlapping or redundant coordinates. If a pair of coordinates wereoverlapping they were merged into one continuous length. The merged CDs coordinates were masked in the respective genomes,
which resulted in only noncoding genomes.
Homology search
After masking coding regions we searched for genomic locations that are conserved across all 10 primate species used in the
study. BlastN 2.6.0 (Altschul et al. 1997) with parameter e < 0.001 was used to determine homologous regions in a wholegenome chain search starting from the most closely related species pair used in the study (human and gorilla). The resulting
sequence hits were ltered to obtain the best hit with lowest e-value (multiple hits per sequence occurrence were ltered only tokeep the best hit scenario with lowest e-value) and these sequences (the reference genome sequences [human]) was used as thequery to search in the evolutionarily next closest species. Likewise a chain search was followed step by step through the 10
primate species considered in the study. Mitochondrial DNA was removed from the analysis.

fi

fi

fi

7

iDarwin AS 0022 (2022 AD)

Volume 2

In setting the percentage conservation threshold for the CNSs the methodology introduced by Babarinde and Saitou (2013)
was followed. Initially the nucleotide sequences of 1:1 orthologs of protein coding genes of human and mouse lemur wereextracted from Ensembl release 94 (Yates et al. 2020). After Blastn search (e < 0.001) of these gene nucleotide sequences theaverage conservation percentage identity was obtained. This average percentage identity was used as the cut off threshold to
extract the primate common CNSs. In other words the primate common CNSs had to be present in all 10 species used in theanalysis while having a percentage identity exceeding the protein coding gene conservation level of the two most distant species
used in the study (human and mouse lemur).
The determined commonly conserved sequences were classi ed into two groups based on how many conserved regions
overlapped with annotated noncoding RNA gene regions in human. The annotation data for ncRNA genes is based on Ensembl
version 94 (Yates et al. 2020). Any conserved noncoding sequence that overlapped with annotated ncRNA genes were classi ed
as primate common ncRNA-gene-CEs (ncRNA-gene-Conserved Elements) and remaining sequences are referred to as CNSs
(Conserved Noncoding Sequences).
Determining histone modi cation and DNaseI hypersensitive site overlaps for primate common CNSs and ncRNA-geneCEs
The chip-seq data for histone modi cations (H3K4Me1, H3K4Me2, H3K4Me3, H3K9ac, H3K9Me3, H3K27ac, H3K27Me3,
H3K36Me3, H3K79Me3, H4K20Me1) were downloaded from UCSC (http://genome.ucsc.edu/encode/downloads.html) which isbased on a direct submission from ENCODE production data (Human Genome Build 37 (hg19)). Speci cally cell-line GM12878
data for histone modi cation broad peaks were considered. GM12878 cell-line is mentioned as having a relatively normal
karyotype. Also DNaseI hypersensitive site hotspot data for 2 replicates were downloaded from the above mentioned link.
Since the originally submitted ENCODE data was based on Grch37 human genome build, the coordinates needed to be lifted to
the latest version of human genome which is used in the analysis (Grch38). UCSC browser lift-over (https://genome.ucsc.edu/cgibin/hgLiftOver?hgsid=696327251_5kBwkhosCaqhOqgcpSUNVALQIBaf) was used to convert the ENCODE data coordinates tothe latest coordinates.
The converted coordinates of histone modi cations and DNaseI hypersensitive sites were checked for overlaps with theprimate common CNSs and ncRNA-gene-CEs. The median signal strength value was considered as the most reliable measure of

fi

fi

fi

fi

fi

fi

fi

8

iDarwin AS 0022 (2022 AD)

Volume 2

the functional signature strength across CNSs and ncRNA-gene-CEs, since the distribution of the signal strength values were notnormally distributed. Normality test for skewness and kurtosis was performed with shapiro-wilks test.
In order to determine the difference between conserved regions with respect to neutrally evolving regions in the genome,
we also picked random samples of sequence coordinates from rest of the noncoding regions of the human genome. We made 25
coordinate datasets for each histone modi cation. These random regions were picked on a very stringent criteria that they do not
overlap with coding regions, already determined conserved noncoding regions in the current study or repeat regions of the humangenome. These random data sets contain same number of sequences as the original dataset of CNSs and ncRNA-gene-CEs. Also
the random sequences were extracted in such a way to have the same length and same chromosome as a particular CNS orconserved ncRNA.
Selection pressure on primate common ncRNA-gene-CEs and CNSs
We downloaded 1000GP human single nucleotide polymorphism data (ftp://ftp.ensembl.org/pub/release-94/variation/vcf/
homo_sapiens/) for Yoruba population in Ibadan, Nigeria (YRI), Han Chinese in Beijing (CHB), China and individuals with
Northern and Western European ancestry (CEU) collected from Utah. Then we determined how many SNPs overlap with primate
common CNSs and primate common ncRNA-gene-CEs. Random coordinate sets from human genome were picked in order tocompare between conserved regions and non-conserved regions. The random samples have the same number of instances, samelength, and were picked from the same chromosome as the CNSs or the ncRNA-gene-CEs. The random samples were normalized
and tested for statistical signi cance by chi-square test.
Evolutionary rates of CNSs and ncRNA-gene-CEs
The identi ed CNSs and ncRNA-gene-CEs were concatenated into two sequence groups and the evolutionary rates for all
primate common CNSs and ncRNA-gene-CEs at each branch were estimated by constructing neighbour-joining trees (Saitou andNei 1987) with 1000 bootstrap replications. Evolutionary distances were calculated with maximum composite likelihood methodwith MEGA 5.0 (Tamura et al. 2011). The objective of this analysis was to determine if these CNSs and ncRNA-gene-CEs havegone through a fast-evolving phase before stabilizing and which branches show accelerated evolution.
Functional classi cation of genes in close proximity to CNSs and ncRNA-gene-CEs

fi

fi

fi

fi

9

iDarwin AS 0022 (2022 AD)

Volume 2

First we determined the orthologous genes that are closest to CNSs and ncRNA-gene-CEs. These closest genes in the reference
genome (human) were considered as the likely target gene, if they had an orthologous gene in the most distant outgroup species
used in the study (mouse lemur). For these gene sets we determined functional enrichment via PANTHER14.1 classi cation
system (Thomas et al. 2003).
Likely target gene tissue expression patterns
In order to determine if the likely target gene tissue expression patterns follow the same trend as Gene Ontology (GO) analysis
we retrieved Riken Fantom 5 project data for adult human and fetal data for 76 tissues through EBI (https://www.ebi.ac.uk/gxa/home) expression atlas. For this analysis we used 8089 likely target genes for CNSs and 7421 likely target genes for ncRNAgene-CEs that were found in the expression data. The genes that had any sign of expression were considered for further analyses.
The number of genes expressed for brain and nervous system related functions and several tissues related to housekeeping
functions were considered as two potential groups for better comparison. Also we constructed 10 random gene sets for each group(CNSs and ncRNA-gene-CEs) for clarity on random expectation. The statistical signi cance of the data and random samples
were determined by one-sample t-test with statistical signi cance level of 0.05.
Clustering patterns of CNSs and ncRNA-gene-CEs
We determined the tendency of primate common CNSs and conserved ncRNA occurring in clusters. This analysis was conductedby considering groups of CNSs and ncRNA-gene-CEs that clustered with one likely target gene (considering closest orthologous
gene as described before). This analysis determines how many CNSs or conserved ncRNAs are associated with its closest
orthologous target gene and their occurrence trends in the reference genome. We classi ed the CNSs and ncRNA-gene-CEs into
two groups for further comparisons, that is genes with 1-2 CNSs / 1-2 ncRNA-gene-CEs and genes with >6 CNSs / >6 ncRNAgene-CEs.
Brain related tissue expression levels for genes associated with CNSs and ncRNA-gene-CE clusters
Furthermore we determined if genes with many CNSs or ncRNA have difference in their expression level in diverse brain related
tissues. Here we used genes expressed in four tissues namely amygdala, caudate nucleus, cerebellum and spinal cord. To have anelaborate view on tissue expression we considered genes that showed any kind of expression without a baseline TPM (Transcripts
per Million) threshold. The objective of this analysis is to see any differences or similarities in gene expression patterns for CNSs
and conserved ncRNA in clusters and how the 2 groups are functioning on the genomic level. This helps to shed light upon

fi

fi

fi

fi

10

iDarwin AS 0022 (2022 AD)

Volume 2

understanding the evolutionary dynamics of CNSs and conserved ncRNA with respect to expression. Statistical tests for
signi cance were determined by Mann-Whitney U test.
CNSs and ncRNA-gene-CEs relation to experimentally veri ed enhancers, promoters and CTCF binding sites
We determined if our primate common CNSs and ncRNA-gene-CEs are actually related to predicted enhancer, promoter and
CTCF binding sites in (ensemble.org/info/genomeensembl.org/info/genome/funcgen/regulatory_build.html). Based on thepremise that, if CNSs are actually governing gene regulation we expect more enhancers to be associated with CNSs. Alsochecking the magnitude of ncRNA-gene-CEs association to enhancers helps to elucidate the functional landscape of conserved
ncRNA as well. CTCF binding sites are considered important and conserved across lineages functioning as insulator elements,
blocking enhancer activity functioning as domain barriers (Hou et al. 2008; Cuddapath et al. 2009; Herold et al. 2012). Promoters
help to get an understanding on which regions have higher tendency to get transcribed, by comparing association of our CNSs
and ncRNA-gene-CEs to documented promoter regions and CTCF binding sites also help to differentiate CNSs and ncRNAgene-CEs and their speci c role in a genome.
We compare our dataset against random regions with same length and genomic location (chromosome) as CNSs or
ncRNA-gene-CEs of the reference genome. Statistical signi cance was determined by one sample t-test.
Also to be con dent that the pattern of representation is not dependent on the initial number difference in CNSs and ncRNAgene-CEs, we further randomly picked 20 samples of 59,870 CNSs (same number as identi ed ncRNA-gene-CEs) and searched
for the signals of enhancers, CTCF binding and promoters in the 20 sampled datasets. Also we determined the functional
categories for the likely target genes of CTCF binding, enhancer and promoter overlapping CNSs and ncRNA-gene-CEs.
Determining the presence of primate deep conserved elements in mammals for more ancient conservation
We tried to determine how many of the primate common conserved sequences are found in mammalian common ancestor. For
this analysis we searched primate common CNSs and ncRNA-gene-CEs separately in rat (Rattus norvegicus) and mouse genomes(Mus musculus) in Ensembl release 94 (with Blastn search (e < 0.001). The sequence homologs that were found in rat and mouse
both were presumed to have originated in mammalian common ancestor. For the mammalian common sequences the likely target
gene GO was determined via PANTHER14.1. Also we looked at the tissue expression patterns of these genes via Riken Fantom 5
project data for adult human and fetal expression atlas through EBI (https://www.ebi.ac.uk/gxa/home).

fi

fi

fi

fi

fi

fi

11

iDarwin AS 0022 (2022 AD)

Volume 2

Have Hominoid speci c conserved non coding elements originated to render speci c functions to hominoid lineage?
We determined the Hominoid lineage speci c elements that are unique to human, gorilla, orangutan and gibbon by searching forhomologs of this group in all primate outgroup species. Any sequence instance that was also found in any of the outgroup species
were removed from the nal data set or any further analyses. Finally we considered only the sequences above 90% percentage
identity for further analyses. These hominoid unique elements were checked for their closest likely target gene related functions
and tissue expression patterns.
Do CNSs and ncRNA-gene-CEs belong to independent categories?
Since we observed similar functional categories for CNSs and ncRNA-gene-CEs we identi ed in the study, it was important toknow if there were any CNSs and ncRNA cross homology sequences causing this scenario. Therefore we searched all theconserved noncoding sequences against itself with Blastn with e<0.001. After removing self-hits and one copy of reciprocal hits,
the remaining hits with a CNS and ncRNA combinations were extracted (4,772 sequence instances). The coordinates of these
CNS, ncRNA cross homology entities were searched for their features with Ensembl feature category based on annotation.

Results
Identi cation and characterization of a highly conserved CNS and ncRNA dataset spanning primates
To generate a conservative, high con dence set of CNS and ncRNA sequences, we screened for conserved noncoding regions
present in 10 primates. This yielded 153,475 primate common conserved noncoding regions. Of these, 59,870 are candidate
ncRNA gene overlaps (overlapping with annotated ncRNAs) while 93,605 sequences were found in other non-coding (Intergenic,
Untranslated Regions [UTR], introns of protein coding genes) regions of the human genome (reference genome used in the
study). Based on these assignments, we will refer to these conserved noncoding regions as ncRNA-gene-CEs and CNSs,
respectively throughout the results section.
Epigenetic pro les of primate CNS and ncRNA gene-CEs
H3K4Me1 is a histone modi cation associated with active enhancer regions (Koenecke et al. 2016; Barakat et al. 2018). The
median signal strength of H3K4Me1 located inside CNSs were slightly higher but not statistically signi cant compared torandom coordinates (Figure 2A). Also another histone modi cation that is associated with activated enhancer regions is

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

12

iDarwin AS 0022 (2022 AD)

Volume 2

H3K27ac. The CNSs have signi cantly higher levels of H3K27ac compared to random samples (p < 0.0001). H3K79 methylation
is known to be associated with developmentally regulated gene expression in multicellular eukaryotes. For this histone
modi cation, both primate common CNSs and ncRNA-gene-CEs have high levels of signal strength compared to random
samples. H3K9ac which is a histone modi cation mark predominantly found in active promoter regions showed high signal
strength in CNSs with respect to random sample (p = 0.003). In general according to our results, CNSs show a higher tendency
to be active enhancer elements associated with regulatory activities.
A

B

Figure 2. (A) Histone modi cation median signal strength for primate common CNSs. (B) Histone modi cation median signal strength forprimate common ncRNA-gene-CEs. Speci cally CNSs show higher signal strength for H3K27ac (histone marks associated with active enhancer
regions) compared to conserved ncRNA and also random expectation. Conserved non-coding RNA shows signi cantly higher signal strength for
H4K20me1. Statistical signi cance was determined via one-sample t-test. (P <0.0001 - ***, P > 0.001 = **, P>0.01 = *).

fi

fi

fi

fi

fi

fi

fi

fi

13

iDarwin AS 0022 (2022 AD)

Volume 2

H3K4Me1 in ncRNA-gene-CEs were statistically signi cantly lower
than random expectation (p < 0.0001), signifying that these primate common
ncRNA-gene-CEs may have less chance of functioning as active enhancers.
Also H3K27ac, another active enhancer histone modi cation showedsigni cantly less strength in ncRNA-gene-CEs compared to random samples.
This result implies that the ncRNA-gene-CEs in this case may not be
associated with active enhancers as CNSs.
One interesting result is that, H4K20Me1 shows signi cantly higher
levels of association with primate ncRNA-gene-CEs compared to CNSs
(Figure 2B). This histone modi cation is involved in DNA replication anddamage repair. CCND1 is the rst long ncRNA that was identi ed as beingtranscribed in response to DNA damage signals (Wang et al. 2008; Klein andAssoian 2008). LincROR has been identi ed as a P53 repressor in response to
DNA damage (Zhang et al. 2013). In addition several other studies haverevealed ncRNA association to DNA repair and replication (Ge and Lin 2014; Table 1. Number of histone modi cation signals
Hawley et al. 2017). The fact that we found primate common ncRNA-gene- located inside CNSs or ncRNA-gene-CEs
CEs had a high signal strength for H4K20Me1 further signi es association ofncRNA-gene-CEs with DNA damage repair. The number of histone modi cation sites found inside CNSs and ncRNA-gene-CEs
are given in Table 1.
DNaseI hypertensive sites (DHSs) in conserved noncoding regions
We found that CNSs have signi cantly less abundance of DNaseI hypersensitive sites associated with them (p<0.0001),
compared to random expectation. Whereas ncRNA-gene-CEs show an opposite trend where they have more DNaseI sites in them(p<0.0001), compared to random samples (Figure 3). DHSs signify open chromatin regions according to many reports (Song etal. 2011; Madrigal and Krajewski 2012; Li and Cui 2018). Therefore this result implies that the primate common CNSs we
identi ed are located in regions with less open chromatin conformation compared to random regions in the human genome. But

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

14

iDarwin AS 0022 (2022 AD)

Volume 2

primate common ncRNA-gene-CEs have more DHSs compared to the random expectation implying a higher tendency forncRNA-gene-CEs to be in open chromatin conformation.
Purifying selection on conserved noncoding regions
In order to test for the level of selection of primate common
CNSs and ncRNA-gene-CEs we carried out the derived allele
frequency analysis. Functional regions under purifying selection
are expected to have lower levels or lower frequency of derived
alleles than regions that are neutrally evolving. Our results showthat the primate common CNSs and ncRNA-gene-CEs both havea high percentage of lower derived alleles than neutrally evolving
random sequences. Alleles with lower frequency levels (DAF<
0.1) in both CNSs and ncRNA-gene-CEs show signi cantly
higher level of abundance than expected. (Z-test p<0.00001).
Also it is important to note that high frequency alleles areless abundant in evolutionarily conserved regions than neutrally
evolving background sequences of the human genome.
Abundance levels of SNPs with derived allele frequency>0.9 are Figure 3. Dnase I hypersensitive sites associated with primate
common CNSs and ncRNA-gene-CEs. The sites that are found insidesigni cantly higher (z-test p<0.00001) in neutrally evolving conserved regions were considered. The random expectation was
regions compared to conserved regions in our results. Also it is determined by 25 random samples from human genome that are notclear that ncRNA-gene-CEs are under slightly higher levels of conserved and are presumably neutrally evolving. The results were
statistically signi cant (p<0.0001).
purifying selection than CNSs. This scenario is uniform withboth Yoruba and Han Chinese population (Figures 4 and 5). With Yoruba population, CNSs had a percentage level of 47.65%(Figure 4A) for lower allele frequency whereas ncRNA-gene-CEs had 49.81% (Figure 4B). Han Chinese and European (Figures
5 and 6) SNP data also showed that ncRNA had a higher percentage level (24.3% and 50.02%, respectively) compared to CNSs
for lower derived alleles (<0.1). For Yoruba population the statistical signi cance levels of CNSs and ncRNA-gene-CEs SNP

fi

fi

fi

fi

15

iDarwin AS 0022 (2022 AD)

Volume 2

distribution from the random expectation are p < 2.05854E-49 and p <8.91508E-74 (chi-square test) respectively. In other words
the observed SNP distribution in CNSs and ncRNA-gene-CEs has a very low probability of being a chance distribution.
These results show that generally, conserved non-coding regions are under purifying selection and some conserved noncoding regions, such as primate common ncRNA-gene-CEs in this analysis are under a slightly higher selective pressure than
CNSs. It is already well known that conserved non-coding regions are functionally important and disruption or mutations of these
conserved regions can lead to detrimental effects.
A

B

Figure 4. Derived allele frequency analysis for primate common CNSs and primate common ncRNA-gene-CEs using Yoruba population. (A)Derived Allele Frequencies for CNSs with Yoruba population. (B) Derived Allele Frequencies for ncRNA-gene-CEs with Yoruba population
data. Random coordinates from the human genome build Grch38 were picked and considered as neutrally evolving regions compared to conserved
regions. The 1000GP SNP data was used for this analysis. CNSs and conserved non-coding RNA both have higher percentage of SNPs for lower
derived allele frequency level <0.1 compared to random expectation. The results are statically signi cant at p <0.05 (z-test).

fi

16

iDarwin AS 0022 (2022 AD)

A

Volume 2

B

Figure 5. Derived allele frequency for CNSs and ncRNA-gene-CEs with Han Chinese population. (A) Derived Allele Frequencies for CNSs
with Han Chinese population (B) Derived Allele Frequencies for ncRNA-gene-CEs with Han Chinese population data.

Primate common ncRNA-gene-CEs also show a higher level of selection or in other words, less tolerant of higher
frequency mutations. Majority of the primate common ncRNA-gene-CEs we identi ed in this analysis were long non-coding
RNA. It has been shown in many studies that long non-coding RNA can also function in gene regulation controlling gene
expression (Bao et al. 2013), therefore it can be expected that conserved ncRNA to be under purifying selection.
Accelerated evolution of CNSs and ncRNA-gene-CEs
We found primate common CNSs and ncRNA-gene-CEs have gone through a phase of accelerated evolution in several different
stages before getting stabilized. The human-gorilla common ancestor sequences showed 4 times faster evolutionary ratecompared to human-gorilla-orangutan common ancestor for both CNSs and ncRNA-gene-CEs (Figure 7A, 7B). Also weobserved that Hominoidae common ancestor showed accelerated evolution in CNS sequences after diverging from Hominoidae-

fi17

iDarwin AS 0022 (2022 AD)

Volume 2

old world monkey common ancestor. Also old world monkeys show about 2 fold acceleration after diverging from Hominoidaeold world monkey common ancestor.
A

B

Figure 6. Derived allele frequency for CNSs and ncRNA-gene-CEs with European population. (A) Derived Allele Frequencies for CNSs with
European population. (B) Derived Allele Frequencies for ncRNA-gene-CEs with European population data.

Functional classi cation of genes associated with CNSs and ncRNA-gene-CEs
Functional classi cation for the closest orthologous gene for CNSs showed a pattern where majority of the genes were associatedwith neuron development (Table 2A). Surprisingly the closest orthologous gene to ncRNA-gene-CEs also followed the same
pattern (Table 2B) per gene ontology. The genes closest to ncRNA-gene-CEs were also involved in neuron development andbrain related functions. Even though the fold enrichment (genes observed, over expected number) was slightly higher for genes
closest to CNSs compared to ncRNA-gene-CEs, this uniform result signi es that these ncRNA-gene-CEs may be involved in cis
regulation of these genes. It de nitely is intriguing that these primate common CNSs and ncRNA-gene-CEs follow similar
patterns in their potential functions.
Likely target gene tissue expression patterns for primate common CNSs

fi

fi

fi

fi

18

iDarwin AS 0022 (2022 AD)

Volume 2

Table 2. GO terms related with closest genes for CNSs and ncRNAgene-CEs. (A)Gene Enrichment values for likely target genes for CNSs
(B) Gene Enrichment values for likely target genes forncRNA-gene-CEs.

Table 4. GO terms related with closest genes for mammaliancommon CNSs and conserved ncRNA. (A) Gene Enrichment values
for likely target genes for CNSs (B) Gene Enrichment values forlikely target genes forncRNA-gene-CEs.

Majority of the closest proximity genes for both CNSs and ncRNA-gene-CEs
were related to brain and nervous system related tissues. Amygdala, brain meninx,
occipital lobe and spinal cord among others showed the highest fraction of
expressed genes for both categories of CNSs and ncRNA-gene-CEs (Figure 8).The
expressed gene fractions are statistically signi cant in all cases compared to random
Table 3. Number of CNSs and ncRNA-gene- samples (p<0.00001).This pattern is same as what was observed in the GO analysis
CEs found in hominoidae, primate, and where high level of enrichment was shown for nervous system development. Also
mammal common ancestors.

fi

19

iDarwin AS 0022 (2022 AD)

Volume 2A

B

Figure 7. (A) CNSs show faster evolution at different stages in phylogeny. (B) Evolutionary rates of ncRNA-gene-CEs in primate lineage. The
branches with faster substitution (>=8*10-10per site per year) are highlighted by a red bar. Primate common CNSs went through acceleration at
Human-Gorilla, Hominoidae and Old world monkey common ancestor.Conserved ncRNA gene sequences also show acceleration during primate
evolution. The mutation rate is as per site per year.

random gene sets that are not closest to CNSs or ncRNA-gene-CEs, showed signi cantly lower levels of genes expressed in brain
related tissues compared to closest likely target genes. Also there is a signi cant difference in CNS and ncRNA percentage gene
expression for brain related tissues, that is CNSs have a slightly higher tendency to harbour brain or nervous system related genes
as the adjacent likely target gene compared to ncRNA-gene-CEs(P = 0.00799, one tail t-test).
About 40% of primate common sequences are found in Mammalian common ancestor

fi

fi

20

iDarwin AS 0022 (2022 AD)

Volume 2A

B

Figure 8. Primate common CNSs and ncRNA-gene-CEs closest gene expression patterns. (A) The percentage of expressed genes for CNS. (B)The percentage of expressed genes for ncRNA-gene-CEs. Grey, black bars represent brain related and house-keeping tissues respectively. Greenbars signify the random expectation. Statistical signi cance of all results is p<0.00001.

We found only 35,972 CNSs and 23,549 ncRNA-gene-CEs in the mammalian common ancestor (Table 3). This means only 38%
of CNSs and 39% of the ncRNA-gene-CEs found as primate common sequences already existed in the mammalian common
ancestor, which are very ancient sequences. Therefore majority (about 60%) of primate common sequences we identi ed in thisstudy appear to have solely originated in the primate common ancestor.
The closest likely target gene functional classi cation for these sequences revealed that mammalian common ncRNA-geneCEs (while assuming a cis activity for these sequences) are related to regulation of synapse structure and activity (Table 4).
Mammalian common CNSs too show similar pattern of relatedness to central nervous system but with less fold-enrichment
meaning that these ancient noncoding conservation may not be the sole determinant of brain function in primates. i.e primate
evolution has taken a new turn by recruiting or generating new regulatory elements to suits its lineage necessities.

fi

fi

fi

21

iDarwin AS 0022 (2022 AD)

Volume 2

Hominoidae speci c CNSs and ncRNA-gene-CEs are less associated withimmune responses but more with neuron development
We identi ed a total of 12,481 elements that presumably originated and areconserved only in the hominoidae lineage. Of the total elements 3,363 werencRNA-gene-CEs, while 9,118 were CNSs. In general terms these lineage
speci c elements also cluster close to genes that are predominantly related withneuron development and differentiation (Table 4).
Table 5. Broad feature categories of CNS-ncRNA
cross homology sequences. This analysis took a total CNSs and ncRNA-gene-CEs are mostly mutually exclusive groups ofof 4,772 cross homology elements and searched for conserved elements
coordinate overlaps in Ensembl feature categories.

We found that 4,772 conserved elements showed CNS-ncRNA
cross homology, In other words only 3.10% of all the identi ed
conserved non coding elements fell into this category. The featurecategories of these cross homology elements were predominantly
categorized as miRNA target sites (Tables 5 and 6). Also it’s
important to note that miRNA target site coordinates are all nonoverlapping and are located in diverse locations in the reference
genome. This result shows that the CNS, ncRNA-gene-CEs
predominantly fall into mutually exclusive groups which further
clari es that the previously observed similar functional
categorization of the CNS and ncRNA-gene-CEs could not havebeen a result of cross homology sequences.

Table 6. GO terms related with closest genes for hominoidae speci cconserved elements. (A) Overrepresented functional classi cations oflikely target genes. (B) Underrepresented GO terms.

fi

fi

fi

fi

fi

fi

fi

22

iDarwin AS 0022 (2022 AD)

Volume 2

CNSs and ncRNA-gene-CEs both show less enrichment in CTCF binding and higher enrichment in enhancer regions
compared to random regions in the human genome
We see signi cantly higher levels of enhancers in CNSs and ncRNA-gene-CEs
compared to random expectation (p<0.05) (Figure 9A). This further establishes
the fact that primate common CNSs and ncRNA-gene-CEs play a signi cantrole as enhancer elements. Interestingly we nd that both CNSs and ncRNAgene-CEs show less enrichment for CTCF binding compared to other random
regions in the human genome (p<0.05), signifying these identi ed conserved
regions have a less tendency to be insulator or repressive domains (Figure 9B).
But one important feature to note is that more promoter regions are associatedwith ncRNA-gene-CEs in our dataset compared to CNSs, re ecting moretranscriptional activity (p<0.05) of ncRNA-gene-CEs compared to CNSs
(Figure 9C).
In comparison the randomly picked (same number of CNSs as ncRNA-geneCEs in the original data set – sampled 20 times with replacement) CNSs withncRNA-gene-CEs, showed that number of CNSs that harboured enhancer were
not statistically signi cant compared to ncRNA-gene-CEs, meaning that thesencRNA-gene-CEs and CNSs have equal tendency to have enhancer elements(Figure 9A). Also sampled CNSs showed no statistical signi cance inabundance of CTCF binding sites compared to ncRNA-gene-CEs, signifying
Figure 9. Enhancer, CTCF binding and promoter elements in primate common CNSs
and ncRNA-gene-CEs according to Ensembl regulatory track data. (A) Enhancer
elements overlapping CNSs, sampled CNSs, ncRNA-gene-CEs and random regions. (B)CTCF binding elements overlapping CNSs, sampled CNSs, ncRNA-gene-CEs and random
regions. (C) Promoter elements overlapping CNSs, sampled CNSs, ncRNA-gene-CEs andrandom regions. Statistically signi cant (p <0.05) combinations are depicted by * and nonsigni cance by NS.

fi

fi

fl

fi

fi

fi

fi

fi

fi

23

iDarwin AS 0022 (2022 AD)

Volume 2that CNSs and ncRNA-gene-CEs have equally less tendency to function as insulator
elements (Figure 9B). But promoter regions were signi cantly less abundant in CNSs
than ncRNA-gene-CEs signifying a difference in transcription activity in CNSs andncRNA-gene-CEs (p is < .00001) (Figure 9C) which also agrees with the comparison
we made to randomly picked non conserved regions of the genome to primate common
CNSs and ncRNA-gene-CEs. Also the enhancer and CTCF overlapping CNSs showed
higher enrichment related to nervous system and brain related structural and functional
Table 7. CNSs and ncRNA-gene-CEs
overlapping Ensembl veri ed enhancers, GO terms while promoter overlapping CNSs and ncRNA-gene-CEs were more relatedwith housekeeping structural and functional terms (Table 7). This further clari es
CTCF bindng sites and promoters.
experimentally veri ed enhancer related CNSs are related to brain and nervous system
function as we have noted earlier.
Expression levels of genes associated with clustered CNSs and ncRNA-gene-CEs reveal signi cant difference based onclustering pattern
We found 3380 and 3457 likely target genes in total, associated with 1-2 CNSs and >6 CNSs respectively. 1-2 CNS group showed
more target genes being expressed in amygdala and spinal cord (Figure 10). Also in general 1-2 CNS group showed more genes
being expressed compared to genes associated with more than 6 CNSs. Also we closely observed the expression levels of thesegenes and found that there is a signi cant difference in the gene expression levels for each of the 4 tissues for the 2 groups. More
expression levels were seen for genes associated with 1-2 CNSs for all for tissues (amygdala, caudate nucleus, cerebellum andspinal cord) compared to CNSs in clusters of more than 6 conserved regions (Figure 10A).
Figure 10 (see next page) (A) Expression levels for genes associated with clustered CNSs.The CNS_group represents grouping pattern thatwas considered, i.e 1-2 means genes with 1or 2 CNSs, whereas >6 means genes with more than 6 CNSs.The statistical signi cance wasdetermined by Mann-whitney U-test. The x-axis represents gene expression level for each of these groups for several brain related tissues (absolutevalues are used in TPM (Transcripts per Million)).
(B) Expression levels for genes associated with clustered conserved ncRNA. The ncRNA group represents grouping pattern that wasconsidered,i.e 1-2 means genes with 1or 2 conserved ncRNAs, whereas >6 means, genes with more than 6 conserved ncRNAs. The x-axis represents
gene expression level for each of these groups for several brain related tissues.

fi

fi

fi

fi

fi

fi

fi

24

iDarwin AS 0022 (2022 AD)

A

Volume 2

B

25

iDarwin AS 0022 (2022 AD)

Volume 2ncRNA-gene-CEs also followed the same pattern as
CNSs where 1-2 conserved ncRNA cluster related genes
showed higher expression levels compared to larger
clusters (Figure 10B). Also further investigation showedthat the genes with 1-2 CNSs or ncRNA-gene-CE
clusters were very much similar in their expression
levels across other tissues we considered for thisanalysis. It appears that multiple long blocks of ncRNAgene-CEs or CNSs are not necessarily required toachieve high expression levels of the target genes.
Clustering patterns of CNSs and ncRNA-gene-CEs
with likely target genes

Figure 11. percentage occurrence of gene-CNS gene-ncRNA clusters ofprimate common conserved noncoding regions. The gure colour legend
represents all cases of gene-CNS or ncRNA associations. i.e 1 gene - 1CNS/1
ncRNA association, 1 gene -multiple CNSs/ncRNA associations and percentage
occurrence.

In majority of the cases, one conserved noncoding
region was associated with one likely target gene, forboth CNSs and ncRNA-gene-CEs (Figure 11). CNSs
and ncRNA-gene-CEs showed no signi cant difference
regards to occurrence in clustering, implying that it isequally likely for CNSs and conserved ncRNA to occur
in clusters.

Discussion
We have identi ed a set of highly conserved ncRNAs and CNSs from primates. Many are also found in other mammals, and ouranalyses indicate these are under selective constraint, giving us con dence that they are likely to be functional elements.

fi

fi

fi

fi

26

iDarwin AS 0022 (2022 AD)

Volume 2

We assessed the probable function of our CNS and ncRNA dataset by looking at the closest adjacent gene. This is frequently done
for CNSs, on the basis that their mode of action is local, and in cis (Nelson and Wardle 2013, Vavouri et al. 2007), while we
expected no clear signal for candidate ncRNAs on the basis that these are more likely to act as trans elements. We were therefore
surprised to nd that the GO term enrichment of adjacent genes for both CNSs and our candidate ncRNA-gene-CEs was similar.
One possibility is that the expression from these ncRNA-gene-CEs may be of type seen for some enhancer elements, where
expression may open up genomic regions, making them accessible for cis-action. This phenomenon is well documented (Shen etal. 2018; Kim and Shiekhattar 2015), and our data suggest this may be prevalent across transcribed noncoding elements, thus
blurring the boundaries between DNA-based cis-acting elements and trans-acting ncRNA genes.
Despite these apparent similarities, we nevertheless nd distinct and distinctive epigenetic signatures that appear to delineate theexpressed and unexpressed elements in our study. These observations in turn suggest that it may be possible to use epigenetic
signatures in the identi cation of functional ncRNAs and CNSs. This may provide a major additional tool in noncoding element
identi cation, particularly as it is dif cult to con dently identify these without comparative genomic data. The comparative
approach aids in building high-con dence annotations as sequence conservation is a strong signal of functional constraint.
However, for species speci c regulatory elements, which may be central to phenotypic change (King & Wilson 1975), epigenetic
signatures may help in the identi cation of newly-emerged noncoding sequences.
We identi ed 153,475 conserved noncoding sequences that are common in the 10 primate species used in the study. We
designated 59,870 sequences as ncRNA-gene-CEs and 93,605 sequences as CNSs. CNSs showed higher signal strength for
H3K4Me1 and H3K27ac which are histone modi cations related with active enhancer regions. Therefore we assume that manyof these CNS elements might be functioning as active enhancers. It has been found that conserved elements are associated withactive enhancer regions (Heintzman et al. 2009; Creyghton et al. 2010). Blum et al. (2012) found that 34% of their myoblastenhancers and 36% of myotube enhancers overlapped conserved noncoding sequences. It is currently known that even some of
the ncRNA can also act as enhancer elements (Kim et al. 2010; Natoli and Andrau. 2012; Chen et al. 2017), but our primate
common ncRNA-gene-CEs showed less association with active enhancer element modi cations, signifying that these conserved
elements may be related to other functions. Also we found that CNSs were enriched with histone modi cations associated withregulation of development and active promoters. These ndings with regards to CNSs are in congruence with already
documented evidence (Inada et al. 2003; Bernat et al. 2006; Hettiarachchi et al. 2016).We found that primate common ncRNA-

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

fi

27

iDarwin AS 0022 (2022 AD)

Volume 2gene-CEs had signi cantly higher levels of H4K20Me1 which is a histone modi cation associated with DNA repair than fordevelopmental gene expression and regulation. There are several studies documenting that small and long ncRNA can play a role
in DNA damage repair process (Sharma and Misteli. 2013; Michelini et al. 2017). Also D’Alessandro and Fagagna (2018)
reported that especially long ncRNA plays a role in genome stability. With regards to the primate common CNSs and ncRNAgene-CEs we notice an interleaved functional arena with regards to gene regulation, whereas DNA damage repair is concerned
we see that predominantly ncRNA play a key role, thus implying a slight differentiation in the functions related to genome
stability. Also we found that primate common ncRNA-gene-CEs have a higher tendency to be located in open chromatin regions
compared to rest of the genome. This easily accessible open conformation agrees with facilitating the regulatory functions that
may be related to ncRNA.
Both CNSs and ncRNA-gene-CEs showed lower frequency of derived alleles compared to random regions in humangenome signifying purifying selection. It has been known for a while that conserved non-coding sequences are under purifying
selection and are not merely mutational cold spots (Drake et al. 2006; Katzman et al. 2007; Sakuraba et al. 2008). The primate
common ncRNA-gene-CEs showed a higher percentage level of lower allele frequency compared to CNSs, which in a way
signi es that the ncRNA-gene-CEs are under a strongly constrained selective pressure. It is expected that majority of conserved
regions to be under purifying selection given the assumption that they have been conserved across long divergence times due totheir functional importance, but our result here shows that even among conserved regions some of them can be under a stronger
selective pressure which could be due indispensable functions that cannot be compromised in the genome.
Yet another interesting result of this study is that CNSs and ncRNA-gene-CEs have gone through a phase of accelerated evolution
in several different stages during evolution. The human-gorilla common ancestor sequences showed 4 times faster evolutionaryrate compared to human-gorilla-orangutan common ancestor for both ncRNA-gene-CEs and CNSs. This result suggests thatcertain mutational changes occurred at a faster rate in the human-gorilla common ancestor sequences to facilitate the functional
requirements of the species after divergence. Also we observed that Hominoidae common ancestor showed accelerated evolutionin CNSs after diverging from Hominoidae-old world monkey common ancestor. Also old world monkeys show about 2 foldacceleration after diverging from Hominoidae-old world monkey common ancestor. Going a step further from widely accepted
belief that conserved regions are under constant purifying selection it is important to understand that some conserved regions
change faster prior to getting stabilized and go through purifying selection to maintain what was achieved by accelerated

fi

fi

fi

28

iDarwin AS 0022 (2022 AD)

Volume 2

mutation events. This scenario is possible where species after divergence adopt new set of physiological or morphologicalfeatures that cannot be regulated by already existing functional elements in a genome. Particularly Homininae also referred to as“African great apes” consisting human, chimpanzee and gorilla did particularly experience an increase in brain size andbipedalism (Lovejoy Co. 1980). Without enough evidence it is hard to point that the accelerated evolution in conserved regions
might be responsible for special characteristics of this lineage, but it has been found that certain CNSs are related to neuronaladhesion of human and chimpanzee (Prabakar et al. 2006).
It is known through experimental evidence that CNSs, actually do regulate the closest gene in most scenarios (Sumiyama etal. 2002; Bhatia et al. 2014). Functional classi cation for the ncRNA-gene-CEs and CNSs showed that both groups are enrichedwith genes involved in regulating transcription and neuronal functions. Adopting the most widely accepted classicalunderstanding this implies that ncRNA-gene-CEs and CNSs both might be responsible for similar functions. A recent study
revealed that especially long ncRNA (lncRNA) are associated with development of central nervous system and neurodegenerative
disorders (Wei et al. 2018). Despite the previous belief that lncRNA does not have a function and is a result of mistakes duringtranscription can now be challenged with computational and also some experimental evidence. This result further clari es that
CNSs that were identi ed as ncRNA-gene-CEs in this study might actually have the potential to be related to gene regulation
prior any experimental evidence.
The GO for CNSs and ncRNA-gene-CEs showed evidence that likely target gene of these regions are enriched in brain andnervous system related tissues especially Amygdala, brain meninx, occipital lobe and spinal cord (fetal and adult both). Several
studies document that CNSs are related to brain and nervous system tissues (Meyer et al. 2017). But our nding shows thatncRNA-gene-CEs related genes can also be related to nervous system development rather than house-keeping functions.
We found that 60% of the primate common elements have solely originated in primate common ancestor and could be
accounting for necessary functional requirements in the primate lineage. One interesting feature is that the newly originatedelements also predominantly cluster around genes related to central nervous system. This indirectly implies that further adoptionof conserved elements was required for brain function or re-wiring of molecular pathways related to brain function in the primate
lineage. One signi cant feature that should be noted is, there has been a gradual expansion in the neocortex volume in higher
primates compared to primitive primates such as insectivores and prosimians. Also further expansion in the primate lineage
leading to, great apes having the largest neocortex (Stephen and Andy. 1970) in the animal kingdom. The neocortex is known as

fi

fi

fi

fi

fi

29

iDarwin AS 0022 (2022 AD)

Volume 2

the centre for higher brain functions, such as decision making, reasoning, language and consciousness. The expansion of thisregion and functions related to it in higher level primates such as great apes may have required new regulatory mechanisms thus
newly originated regulatory elements. Even though we cannot directly narrow down all the elements we have identi ed in thisstudy are related to neocortex functions, the tissue expression analyses imply that the conserved elements here are predominantly
related to regions of the brain.
Also hominoidae speci c sequences in this study being related to brain activity further solidify the reasoning above.
Nevertheless it is important to note that mammalian common sequences showed much higher functional fold enrichment for
central nervous system related activity compared to primate common and hominoidae speci c elements. Could this mean thatbasic building blocks for proper functionality and structure of central nervous system occurred way before primates? This issomething worth looking at in more detail.
Our ndings in this study have shed light upon primate common, mammalian common and hominoidae speci c CNSs andncRNA-gene-CEs from numerous functional perspectives and have provided evidence that both groups are important for proper
functionality of the genomes while signifying ncRNA-gene conservation may also be indispensable to genomes than we give
credit for.

References
ALTSCHUL Stephen F., MADDEN Thomas L., SCHÄFFER Alejandro A., ZHANG Jinghui, ZHANG Zheng, MILLER Webb, and LIPMAN David. J. (1997) Gapped BLAST and
PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research, vol. 25, pp. 3389-3402.
ANDERSSON Robin, …, FORREST Alistair R. R., CARNINCI Piero, REHLI Michael, and SANDELIN Albin (2014) An atlas of active enhancers across human cell types andtissues. Nature, vol. 507, pp. 455-461.
AWAN Hassaan Mehboob, SHAH Abdullah, RASHID Farooq, and SHAN Ge (2017) Primate-speci c long non-coding RNAs and microRNAs. Genomics, Proteomics &Bioinformatics, vol. 15, pp. 187-195.
BABARINDE Isaac Adeyemi (2021) Conserved noncoding sequences: Evolving puzzles. iDarwin, vol. 1, pp. 3-36.
BAO Jianqiang, WU Jingwen, SCHUSTER Andrew S., HENNIG Grant W., and YAN Wei (2013) Expression pro ling reveals developmentally regulated lncRNA repertoire in themouse male germline. Biology of Reproduction, vol. 89, Article 107.
BARAKAT Tahsin Stefan, HALBRITTER Florian, ZHANG Man, RENDEIRO André F., PERENTHALER Elina, BOCK Christoph, and CHAMBERS Ian (2018) Functional
dissection of the enhancer repertoire in human embryonic stem cells. Cell Stem Cell, vol. 23, pp. 276-288.

fi

fi

fi

fi

fi

fi

fi

30

iDarwin AS 0022 (2022 AD)

Volume 2

BERNAT John A., aCRAWFORD Gregory E., OGURTSOV Aleksey Y., COLLINS Francis S., GINSBURG David, and KONDRASHOV Alexy S. (2015) Distant conserved
sequences anking endothelial-speci c promoters contain tissue-speci c DNase-hypersensitive sites and over-represented motifs. Human Molecular Genetics, vol. 15, pp.
2098-2105.
BHATIA Shipra, MONAHAN Jack, RAVI Vydianathan, GAUTIER Phillippe, MURDOCH Emma, BRENNER Sydney, van HEYNINGEN Veronica, VENKATESH Byrappa, andKLEINJAN DIRK A. (2014) A survey of ancient conserved non-coding elements in the PAX6 locus reveals a landscape of interdigitated cis-regulatory archipelagos.
Developmental Biology, vol. 387, pp.214-228.
BLUM Roy, VETHANTHAM Vasupradha, BOWMAN Christopher, RUDNICKI Michael, and DYNLACHT Brian D. (2012) Genome-wide identi cation of enhancers in skeletal
muscle: the role of MyoD1. Genes & Development, vol. 26, pp. 2763-2779.
CHEN Grace Y., SATPATHY Ansuman T., and CHANG Howard Y. (2017) Gene regulation in the immune system by long noncoding RNAs. Nature Immunology, vol. 18, pp.
962-972.
CLARKE Shoa L., VANDERMEER Julia E., WENGER Aaron M., SCHAAR Bruce T., AHITUV Nadav, and BEJERANO Gill (2012) Human developmental enhancers conserved
between deuterostomes and protostomes. PLoS Genetics, vol. 8, e1002852.
CREYGTON Menno P., …, and JAENISCH Rudolf (2010) Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proceedings of the National
Academy of Sciences of the United States of America, vol. 107, pp.21931-21936.
D’ALESSANDRO Giuseppina and di FAGAGNA Fabrizio d’Adda (2018) Long non-coding RNAs in the control of genome stability and cancer phenotypes. Non-coding RNA
Investigation, vol. 2, article 13.
DRAKE Jared A., …, DERMITZAKIS Emmanuel T., and HIRSCHHORN Joel N. (2005) Conserved noncoding sequences are selectively constrained and not mutation cold spots.
Nature Genetics, vol. 38, pp. 223-227.
ELGAR Greg (2009) Pan-vertebrate conserved non-coding sequences associated with developmental regulation. Brie ngs in Functional Genomics, vol. 8, pp. 256-265.
The ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, vol. 489, pp. 57–74.
GE Xin Quang and LIN Haifan (2014) Noncoding RNAs in the regulation of DNA replication. Trends in Biochemical Sciences, vol. 39, pp. 341-343.
GLAZKO Galina V. and NEI Masatoshi (2003) Estimation of divergence times for major lineages of primate species. Molecular Biology and Evolution, vol. 20, pp. 424-434.
GOODMAN Morris, PORTER Calvin A.., CZELUSNIAK John, PAGE Scott L., SCHNEIDER Horacio., SHOSHANI Jeheskel, GUNNELL Gregg, and GROVES Colin P. (1998)
Toward a phylogenetic classi cation of primates based on DNA evidence complemented by fossil evidence. Molecular Phylogenetics and Evolution, vol. 9, pp. 585-598.
GRAUR Dan, ZHENG Yichen, PRICE Nicholas, AZEVEDO Ricardo B. R., ZUFALL Rebecca A., and ELHAIK Ran (2013) On the immortality of television sets: “function” in thehuman genome according to evolution-free gospel of ENCODE. Genome Biology and Evolution, vol 5, pp. 578–590.
HAWLEY Ben R., LU Wei-Ting, WILCZYNSKA Ania, and BUSHELL Martin (2017) The emerging role of RNAs in DNA damage repair. Cell Death and Differentiation, Vol. 24,
pp. 580-587.
HEINTZMAN Nathaniel, …, and REN Bing (2009) Histone modi cations at human enhancers re ect global cell type-speci c gene expression. Nature, Vol. 459, pp. 108-112.
HETTIARACHCHI Nilmini, KRYUKOV Kirill, SUMIYAMA Kenta, and SAITOU Naruya (2014) Lineage-speci c conserved noncoding sequences of plant genomes: their possible
role in nucleosome positioning. Genome Biology and Evolution, vol 6, pp. 2527–2542.
HETTIARACHCHI Nilmini and SAITOU Naruya (2016) GC content heterogeneity transition of conserved noncoding sequences occurred at the emergence of vertebrates. Genome
Biology and Evolution, vol. 8, pp. 3377–3392.
INADA Dan Choffnes, BASHIR Ali, LEE Chunghau, THOMAS Brian C., KO Cynthia, GOFF Stephen A, and FREELING Michael (2003) Conserved noncoding sequences in thegrasses. Genome Research, vol. 13, pp. 2030-2041.
KATZMAN Sol, KERN Andrew W., BEJERANO Gill, FEWELL Ginger, FULTON Lucinda, WILSON Richard K., SALAMA So e R., and HAUSSLER David (2007) Human
genome ultra-conserved elements are ultra-selected. Science, vol. 317, p. 915.

fi

fi

fi

fi

fi

fl

fi

fi

fi

fi

fl

31

iDarwin AS 0022 (2022 AD)

Volume 2

KIM Tae-Kyung, …, and GREENBERG Michael E. (2010) Widespread transcription at neuronal activity-regulated enhancers. Nature, Vol. 465, pp. 182-187.
KIM Tae-Kyung and SHIEKHATTAR Ramin (2015) Architectural and functional commonalities between enhancers and promoters. Cell, vol. 162, pp. 948-959.
KLEIN Eric and ASSOIAN Richard K. (2008) Transcriptional regulation of the cyclin D1 gene at a glance. Journal of Cell Science, vol. 121, pp. 3853-3857.
KOENECKE Nina, JOHNSTON Jeff, HE Qiye, MEIER Samuel, and ZEITLINGER Julia (2017) Drosophila poised enhancers are generated during tissue patterning with the help ofrepression. Genome Research, vol. 27, pp. 64-74.
KRITSAS Konstantinos, WUEST Samuel E., HUPALO Daniel, KERN Andrew D., WICKER Thomas, and GROSSNIKLAUS Ueli (2012) Computational analysis andcharacterization of UCE-like elements (ULEs) in plant genomes. Genome Research, vol. 22, pp. 2455-2466.
LEE Alison P., KERK Sze Yen, TAN Yue Ying, BRENNER Sydney, and VENKATESH Byrappa (2011) Ancient vertebrate conserved noncoding elements have been evolving
rapidly in teleost shes. Molecular Biology and Evolution, vol. 28, pp. 1205-1215.
LI Ren and CUI Xia (2018) Genome-wide mapping of DNaseI hypersensitive sites in tomato. Pp. 367-379 in YAMAGUCHI N. Ed., “Plant Transcription Factors –Methods andProtocols”, Springer.
MADRIGAL Pedro and KRAJEWSKI Paweł (2012) Current bioinformatic approaches to identify DNase I hypersensitive sites and genomic footprints from DNase-seq data.
Frontiers in Genetics, vol. 3, article 230.
MATSUNAMI Masatoshi and SAITOU Naruya (2013) Vertebrate paralogous conserved noncoding sequences may be related to gene expressions in brain. Genome Biology and
Evolution, vol. 5, pp. 140-150.
MELAMED Philippa, YOSEFZON Yahav, RUDNISKY Sergei, and PNUELI Lilach (2016) Transcriptional enhancers: Transcription, function and exibility. Transcription, vol. 7,
pp. 26-31.
MEYER Kyle A., MARQUES-BONET Tomas, and SESTAN Nenad (2017) Differential gene expression in the human brain is associated with conserved, but not accelerated,noncoding sequences. Molecular Biology and Evolution, vol. 34, pp. 1217-1229.
MICHELINI Flavia, …, and di FAGAGNA Fabrizio d’Adda (2017) Damage-induced lncRNAs control the DNA damage response through interaction with DDRNAs at individual
double-strand breaks. Nature Cell Biology, vol. 19, pp.1400-1411.
NATOLI Gioacchino and ANDRAU Jean-Christophe (2012) Noncoding transcription at enhancers: general principles and functional models. Annual Review of Genetics, vol. 46,
pp.1-19.
NELSON Andrew C. and WARDLE Fiona (2013) Conserved non-coding elements and cis regulation: actions speak louder than words. Development, vol. 140, pp. 1385-1395.
POLYCHRONOPOULOS Dimitris, SELLIS Diamantis, and ALMIRANTIS Yannis (2014) Conserved noncoding elements follow power-law-like distributions in several genomes asa result of genome dynamics. PLoS One, vol. 9, e95437.
PRABHAKAR Shyam, NOONAN James P., PÄÄBO Svante, and RUBIN Edward M. (2006) Accelerated evolution of conserved noncoding sequences in humans. Science, vol. 314,
pp. 786-796.
SABER Morteza Mahmoudi, BABARINDE Isaac Adeyemi, HETTIARACHCHI Nilmini, and SAITOU Naruya (2016) Emergence and evolution of Hominidae-speci c coding andnoncoding genomic sequences. Genome Biology and Evolution, vol. 8, pp. 2076–2092.
SAITOU Naruya and NEI Masatoshi (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, vol. 4, pp.
406-425.
SAKURABA Yoshiyuki, …, and Gondo Yoichi (2008) Identi cation and characterization of new long conserved noncoding sequences in vertebrates. Mammalian Genome, vol. 19,
pp. 703-712.
SANDELIN Albin, BAILEY Peter, BRUCE Sara, ENGSTRÖM Pär G., KLOS Joanna M., WASSERMAN Wyeth W., ERICSON Johan, and LENHARD Boris (2004) Arrays ofultraconserved non-coding regions span the loci of key developmental genes in vertebrate genomes. BMC Genomics, vol. 5, article 99.
SHARMA Vivek and MISTELI Tom (2013) Non-coding RNAs in DNA damage and repair. FEBS Letters, vol. 587, pp. 1832-1839.

fi

fl

fi

fi

32

iDarwin AS 0022 (2022 AD)

Volume 2

SHEN Yong and others (2018) Identi cation of a novel enhancer/chromatin opening element associated with high-level γ-globin gene expression. Molecular and Cellular Biology,
vol. 38, e00197-18.
SONG Lingyun, …, and BUNGERT Jörg (2011) Open chromatin de ned by DNaseI and FAIRE identi es regulatory elements that shape cell-type identity. Genome Research, vol.
21, pp. 1757-1767.
SUMIYAMA Kenta, IRVINE Steven Q., STOCK David W., WEISS Kenneth M., KAWASAKI Kazuhiko, SHIMIZU Nobuyuki, SHASHIKANT Cooduvalli, MILLER Webb, andRUDDLE Frank H. (2002) Genomic structure and functional control of the Dlx3-7 bigene cluster. Proceedings of the United States of Americas of the National Academy of
Sciences, vol. 99(2), pp. 780-785.
TAKAHASHI Mahoko and SAITOU Naruya (2012) Identi cation and characterization of lineage-speci c highly conserved noncoding sequences in mammalian genomes. Genome
Biology and Evolution, vol. 4, pp. 641-657.
TAMURA Koichiro, PETERSON Daniel, PETERSON Nicholas, STECHER Glen, NEI Masatoshi, and KUMAR Sudhir (2011) MEGA5: molecular evolutionary genetics analysis
using maximum likelihood, evolutionary distance, and maximum parsimony methods. Molecular Biology and Evolution, vol. 28, pp. 2731-2739.
THOMAS Paul, CAMPBELL Michael J., KEJARIWAL Anish, MI Huaiyu, KARLAK Brian, DAVERMAN Robin, DIEMER Karen, MURUGANUJAN Anushya, and
NARECHANIA Apurva (2003) PANTHER: a library of protein families and subfamilies indexed by function. Genome Research, vol. 13, pp. 2129-2141.
TIPPENS Nathaniel D., LIANG Jin, LEUNG Alden King-Yung, WIERBOWSKI Shayne D., OZER Abdullah, BOOTH James G., LIS John T., and YU Haiyuan (2020) Transcription
imparts architecture, function and logic to enhancer units. Nature Genetics, vol. 52, pp. 1067-1075.
VAVOURI Tanya, WALTER Klaudia, GILKS Walter R., LEHNER Ben, and ELGAR Greg (2007) Parallel evolution of conserved non-coding elements that target a common set ofdevelopmental regulatory genes from worms to humans. Genome Biology, vol. 8, article R15.
WANG Xianting, ARAI Shigeki, SONG Xiaoyuan, REICHART Donna, DU Kun, PASCAL Gabriel, TEMPST Paul, ROSENFELS Michael G., GLASS Christopher K., andKUROKAWA Riki (2008) Induced ncRNAs allosterically modify RNA-binding proteins in cis to inhibit transcription. Nature, vol. 454, pp. 126-130.
WEI Xiaoyan, MA Tengfei, CHENG Yifeng, HUANG Cathay C., WANG Xuehua, LU Jiayi, and WANG Jun (2017) Dopamine D1 or D2 receptor-expressing neurons in the central
nervous system. Addiction Biology, vol. 2, pp. 569-584.
WOOLFE Adam, …, and ELGAR Greg (2005) Highly conserved non-coding sequences are associated with vertebrate development. PLoS Biology, vol. 3, e7.
YATES Andrew, …, and FLICEK Paul (2020) Ensembl 2020. Nucleic Acids Research, vol. 48, pp. D682-D688.
ZHANG Ali, ZHOU Nanjing, HUANG Jianquo, LIU Qian, FUKUDA Koji, MA Ding, BAI Cunxue, WATABE Kounosuke, and MO Yin-Yuan (2013) The human long non-coding
RNA-RoR is a p53 repressor in response to DNA damage. Cell Research, vol. 23, pp. 340–350.

fi

fi

fi

fi

fi

33