Article
Pervasive coex pression of spatially proxim al genes
is buffered at the protein level
Georg Kustatscher
1 ,*
, Piotr Grabowski
2
& Juri Rappsilber
1 , 2 ,**
Abstract
Genes are not randomly distributed in the genome. In humans,
10 % of protein-coding genes are transcribed from bidirectional
promoters and many more are organised in larger clusters. Intrigu-
ingly, neighbouring genes are frequently coexpressed but rarely
functionally related. Here we show that coexpression of bidirec-
tional gene pairs, and closeby genes in general, is buffered at the
protein level. Taking into account the 3 D architecture of the
genome, we find that co-regulation of spatially close, functionally
unrelated genes is pervasive at the transcriptome level, but does
not extend to the proteome. We present evidence that non-
functional mRNA coexpression in human cells arises from stochas-
tic chromatin fluctuations and direct regulatory interference
between spatially close genes. Protein-level buffering likely reflects
a lack of coordination of post-transcriptional regulation of func-
tionally unrelated genes. Grouping human genes together along
the genome sequence, or through long-range chromosome folding,
is associated with reduced expression noise. Our results support
the hypothesis that the selection for noise reduction is a major
driver of the evolution of genome organisation.
Keywords gene expression noise; genome organisatio n; proteomics;
regulatory interferenc e; transcriptomics
Subject Categories Chromatin, Epigenetics, Genomics & Functional
Genomics; Genome-Scale & Integrative Biology
DOI 10 . 15252 /msb. 20177548 | Received 19 January 2017 | Re vised 21 July
2017 | Accepted 24 July 2017
Mol Syst Biol. ( 2017 ) 13 : 937
Introduction
The position of genes in the human genome is not random (Hurst
et al , 2004). Genes are often found in pairs or larger clusters that
tend to be coexpressed (Caron et al , 2001; Lercher et al , 2002;
Trinklein et al , 2004). Some of these coordinate transcription of
genes with related functions, for example histone genes and other
clusters resulting from gene duplication. However, the majority of
closeby, coexpressed human genes appear not to have a higher
functional similarity than random gene pairs (Hurst et al , 2004;
Williams & Bowles, 2004; Li et al , 2006; Purmann et al , 2007;
Michalak, 2008; Xu et al , 2012). For example, 35 DNA repair genes
are transcribed from bidirectional promoters, but none of their
paired genes is involved in DNA repair (Xu et al , 2012). This raises
intriguing questions: Why are functionally unrelated genes clustered
in the genome and how can the cell tolerate their coexpression?
Pioneering work in yeast identified the selection for reduced gene
expression noise as a key driver for the evolution of chromosome
organisation (Batada & Hurst, 2007; Wang et al , 2011). A major
cause of gene expression noise is thought to be the random fluctua-
tion of chromatin domains between an active and inactive state,
causing mRNAs to be synthesised in short, stochastic bursts (Raj
et al , 2006). Clusters of active genes may mutually reinforce their
open chromatin state, minimising stochastic chromatin remodelling,
and thereby reduce expression noise (Batada & Hurst, 2007; Wang
et al , 2011). Similarly, genes flanking bidirectional promoters have
lower expression noise than other genes, even if one of the diver-
gent partners is a noncoding RNA (Wang et al , 2011). Noise-
sensitive genes, such as those encoding protein complex subunits,
are enriched among bidirectional pairs, but neither in yeast nor in
human do any of these pairs encode two subunits of the same
protein complex (Li et al , 2006; Wang et al , 2011). Consequently , it
has been suggested that bidirectional promoters may drive noise
reduction rather than the coexpression of functionally related genes
(Wang et al , 2011).
The noise reduction model not only provides a potential explana-
tion for the occurrence of clusters of functionally unrelated genes,
but also predicts that such genes may be coexpressed (Wang et al ,
2011). In yeast, chromatin-modifying enzymes are major contribu-
tors to gene expression noise (Newman et al , 2006) and chromatin
remodelling drives the incidental coexpression of neighbouring ,
functionally unrelated genes (Batada et al , 2007). This coexpression
may be due to a passive mechanism, whereby random transitions
between open and closed chromatin simultaneously expose all
genes within a chromatin domain to the transcriptional machinery.
Alternatively, for very close genes such as those with bidirectional
promoters, the up- or downregulation of one gene may directly
affect the transcriptional status of its neighbour (Wang et al , 2011).
Indeed, such a “ripple effect” of transcriptional activation has been
observed in yeast and humans (Ebisuya et al , 2008). The noise and
1 Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
2 Chair of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
*Corresponding author . Tel: + 44 131 6517057 ; E-mai l: [email protected]
**Corresponding author. Tel: + 44 131 6517056 ; E-mail: [email protected]
ª 2017 The Authors. Publ ishe d under the terms of the CC BY 4 . 0 licen se Molecular Systems Biology 13 : 937 | 2017 1

expression levels of transgenes also vary with their insertion site, as
a result of both domain-wide effects and interference with individ-
ual neighbouring genes (Gierman et al , 2007; Chen & Zhang, 2016).
Transgenes can also affect the mRNA expression levels of endoge-
nous genes located close to the insertion site (Akhtar et al , 2013).
If the transcription of noise-reduced, clustered genes is unduly
influenced by their neighbours, how can individual genes reach
their optimal expression levels? Notably, gene expression is usually
measured at the mRNA level. However, protein levels are buffered
against certain transcript fluctuations (Liu et al , 2016), such as
those caused by stochastic transcription initiation (Raj et al , 2006;
Gandhi et al , 2011) and genetic variation between individuals
(Battle et al , 2015) and species (Khan et al , 2013). The abundance
of some proteins can also be buffered against gene copy number
variations (Geiger et al , 2010; Stingele et al , 2012; Dephoure et al ,
2014). We therefore speculated that protein abundances may also
be buffered against regulatory interference between genes in close
spatial proximity.
Results
Coexpression of bidirectional gene pairs is buffered at the
protein level
We investigated the expression of 4,188 genes across 60 different
human lymphoblastoid cell lines (LCLs), for which mRNA (Pickrell
et al , 2010) and protein abundances (Battle et al , 2015) have been
reported (Fig 1A, Dataset EV1). These genes are highly expressed in
all human tissues and their promoters are in active chromatin states
(Appendix Fig S1). Although constitut ively active, expression levels
of these “housekeeping” genes vary between LCLs, as a result of
genetic and other differences, including age and growth conditions
(Akey et al , 2007; Stark et al , 2014; Yuan et al , 2015). The LCL cell
line panel has been instrumental in identifying expression quantita-
tive trait loci, that is DNA sequence variants that specifically influ-
ence the expression level of one or more genes (Albert & Kruglyak,
2015). Here, instead of assessing how a gene’s expression level
depends on the genotype, we analyse how it is influenced by the
expression of other, closeby genes. LCLs are a valuable test system
as their genome structure and regulatory elements have been
mapped at unparalleled resolution (Lieberman-Aiden et al , 2009;
Ernst et al , 2011; ENCODE Project Consortium , 2012; Rao et al ,
2014).
First, we analysed gene pairs that are transcribed from bidirec-
tional promoters. These are commonly defined as genes that are
found in head-to-head orientation with < 1 kb between their tran-
scription start sites (TSSs) (Trinklein et al , 2004). Out of 167 such
gene pairs in this dataset, the mRNA abundance s of 31 (19%)
are strongly and significantly co-regulated across LCLs (Pearson’s
correlation coefficien t, PCC > 0.5, BH-adjusted P -value < 0.001).
However, protein co-regulation is attenuated or buffered for 28 of
these (Fig 1B, Appendix Table S1). Literature analysis revealed that
the buffered gene pairs generally have unrelated biological func-
tions, in contrast to the three gene pairs whose co-regulation is
sustained at the protein level (Appendix Table S1).
We next considered the 929 non-bidirectional gene pairs with up
to 50 kb between their TSSs, regardless of their orientation (Dataset
EV2). Although these pairs do not share a promoter region, we find
that 22% have co-regulated mRNA abundances (PCC > 0.5, BH-
adjusted P < 0.001). However, only 3% are also co-regulated at the
protein level (Fig 1B).
Genes with similar functions have co-regulated mRNA and
protein abundances
To confirm that the different impact of gene proximity on mRNA
and protein abundances reflects a biological phenome non, rather
than simply a difference in data quality, we assessed the co-regula-
tion of genes with known functional links, irrespective of their
genomic position. We analysed subunits of the same protein
complex, enzymes catalysing consecutive reactions in metabolic
pathways and proteins with identical subcellular localisations. In all
cases, we observe strong co-regulati on on mRNA and protein levels,
but co-regulation of proteins is significantly stronger than that of
mRNAs (Fig EV1, P < 3 × 10
 16
). Therefore, data quality appears
not to be limiting. Instead, the observed differences between mRNA
and protein co-regulation indicate that post-transcriptional processes
eliminate co-regulation of genes which are related spatially, but not
functionally.
A fraction of closeby genes is enriched for similar functions
Our observation that only 3% of closeby genes have co-regulated
protein abundances appears to contrast with the fact that genes in
close genomic proximity are enriched for similar functions
(The
´ venin et al , 2014). However, functional enrichment does not
exclude the possibility that the bulk of closeby gene pairs does not
share similar functions. For example, we find that co-regulation of
transcripts and proteins from closeby genes is more common than
for random protein pairs (Fig 1B), and this enrichment is highly
significant (3% versus 0.4%, P < 4 × 10
 14
).
To analyse the relationship between gene distance and func-
tion more systematically, we assessed functional associations
between our gene pairs using the STRING database (Szklarczyk
et al , 2017). We considered gene pairs to be functionally associ-
ated if their STRING score, that is the likelihood of the associa-
tion to be biologically meaningful, specific and reproducible, was
> 0.7. Using this comprehensive definition, we find that 4.5% of
closeby gene pairs, that is those with < 50 kb between their TSSs,
are related functionally (Fig EV2A). As observed by The
´ venin
et al , we find this to be a significant enrichme nt over gene pairs
that are farther apart. Likewise, gene pairs from the same chro-
mosome are enriched for similar functions relative to those from
different chromosomes. Nevertheless, the extent of mRNA co-
regulation (22%) strongly exceeds co-function, and mRNA co-
regulation of most closeby gene pairs is not sustained at the
protein level (Fig EV2A).
Notably, a similar analysis in yeast has shown that adjacent
genes tend to have correlated mRNA expression and are statistically
enriched for similar functions (Cohen et al , 2000). However, in
striking agreement with our observations, only about 2% of these
coexpressed neighbouring gene pairs have related functions (Batada
et al , 2007) and only for these is gene order evolutionarily
conserved (Hurst et al , 2002). Coexpression of neighbouring genes
has also been observed in Arabidopsis thaliana , but only a fraction
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Molecular Systems Biology Co-regulation of closeby genes Georg Kustatscher et al
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of the observed cases could be explained through a shared function
(Williams & Bowles, 2004).
Long-range gene co-regulation leads to coordinated mRNA but
not protein expression
The influence of gene distance on co-regulation of transcripts is not
limited to genes in close proximity. As seen in the example of chro-
mosome 11, mRNA co-regulation extends over many megabases but
does not affect protein abundances (Fig 1C). Although co-regulation
generally declines with increasing gene distance, such long-range
effects are unlikely to result from transcriptional interference in cis .
A major co-regulation peak of genes that are more than 50 Mb apart
on chromosome 11 suggests that long-range chromosome folding
may be involved. In agreement with this, all chromosomes have
distinct co-regulation curves (Appendix Fig S2).
The co-regulation map of chromosome 11 shows large patches of
genes whose transcripts are coordinately up- and downregulated
(Fig 1D). Importantl y, no corresponding co-regulation is observed
on the protein level (Fig 1E). However, the mRNA co-regulation
map shows a striking similarity to physical associations observed
for our gene set, as extracted from existing Hi-C data (Rao et al ,
2014; Fig 1F). The Hi-C contact matrix of chromosome 11 is corre-
lated with the mRNA co-regulation map (PCC 0.21, P < 2 × 10
 318
),
but not the protein map (PCC 0.00, P = 0.4). Similar mRNA
co-regulation patches can be observed on other chromosomes
(Fig EV3) as well as between different chromosomes (Fig EV4).
Generally, both intra- and interchromosomal co-regulation patches
AB C
D EF
G
Figure 1 . Spatial proximity of genes affects mRNA but not prote in regulation.
A We analysed previously reported mRNA and protein abundances in 59 lymphoblastoid cell lines (LCLs), relative to a reference sample.
B Genes transcrib ed from bidirectional promoters frequently have co-regulated mRNA abundances, but only a fraction of these also have co-regul ated protein
abundances (left). The same is true for non-bidirectional gene pairs whose transcription start sites (TSS) are < 50 kb apart, irrespective of their orientation (right)
(* P < 0 . 05 ,* * P < 2 × 10
 7
, *** P < 4 × 10
 14
based on Fisher ’ s exact test).
C mRNA co-regulation of gene pairs on chromosome 11 decreases with chromosomal distance over many megabases, but not monotonously. Protein co-regulation is
unaffected by genomic distance .
D mRNA co-regulation map for chromosome 11 showing large patches of co-regulated (brown) and anti-regulated (b lue) gene pairs. Four large, co-regulated patches
are highlighted (i – iv).
E No regulation patches exist on the protein level.
F mRNA co-regulation patches partially coincide with physical associations between genes derived from Hi-C data (Rao et al , 2014 ). Numbers in grey box show the
Pearson correlation between the Hi-C map and mRNA (blue) or protein (red) co-regulation maps.
G Patches i, iii and ii, iv broadly coincide with genome subcompartments A 1 and A 2 , respectiv ely.
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Georg Kustatscher et al Co-regulati on of closeby genes Molecular Systems Biology
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co r re sp on d to a re as w it h i n cr ea s e d H i - C co nt a c ts (A p pe nd ix Table S2).
Some chromosomes have more prominent patches than others
(Fig EV3). Chromosome 19, which is short but exceptionally
gene-dense, is unique in forming a single large co-regulation patch
(Fig EV3C). Importantly, none of these mRNA co-regulation patches
are reflected at the protein level (Figs EV3 and EV4, Appendix Fig
S2). This suggests that regulatory interference between genes that
are close in 3D could be associated with similar non-functional
mRNA co-regulation as observed for neighbouring genes in the
genome sequence.
We next sought to determine which structural features of the
genome give rise to mRNA co-regulation patches. Four large mRNA
co-regulation patches can be observed on chromosome 11 (labelled
i – iv in Fig 1D). Co-regulation patches differ widely in size but often
span many megabases, likely reflecting broad architectural features.
Notably, promoters and enhancers typically interact on a smaller
scale, within topologically associated domains (Gibcus & Dekker,
2013). However, co-regulated groups of genes are more reminiscent
of genome compartments. Genome compartments were first identi-
fied on the basis of long-range interactions mapped by Hi-C, which
showed that open and closed chromatin spatially segregate into two
genome-wide compartments (Lieberman-Aiden et al , 2009). The
compartments containing active and repressive chromatin were
designated A and B, respectively. A high-resolution Hi-C map of the
genome in LCLs subsequently identified that these compartments
segregate further into six subcompartments: A1-2 and B1-4 (Rao
et al , 2014). Genomic loci within each subcompartment tend to be
associated with each other more often than with loci from other
subcompartments, that is they are in closer spatial proximity. We
find that co-regulation patches i and iii of chromosome 11 align with
subcompartment A1 and patches ii and iv align with subcompart-
ment A2 (Fig 1G). These are the two subcompartments of the
genome formed by transcriptionally active chromatin, which is
expected given that we analyse housekeeping genes. Interestingly,
genes across patches i and iii are co-regulated, as are genes across
patches ii and iv, suggesting that co-localisation in subcompart-
ments may contribute to the existence of these patches.
Genes with co-regulated mRNAs co-localise in genome
subcompartments
To assess systematically the overlap of co-regulated gene groups
with genome compartments, we clustered genes by co-regulation.
We found four transcriptome regulation groups T1-4 (Fig 2A and
Dataset EV3), explaining more than 50% of the total variance
(Appendix Fig S3). Transcripts within each group are co-regulated
(Fig 2A and B). Genes from T1 and T2 are strongly enriched for
subcompartments A2 and A1, respectively (Fig 2C). Curiously, they
are anti-correlated, that is when T1 genes are upregulated, T2 genes
tend to be downregulated, and vice versa (Fig 2B). Co-regulated
genes of the T3 and T4 groups are also enriched for A1 and A2
subcompartments, respectively. However, they are independent of
T1 and T2, that is there is neither a positive nor a negative correla-
tion between T1/T2 and T3/T4 (Fig 2B). Therefore, while subcom-
partments A1 and A2 are strongly related to transcriptome
regulation groups, they are not sufficient to explain them.
Genome compartments and subcompartments were defined
solely based on their physical interaction patterns, but also have
A
B
C
D
E
Figure 2 . Transcriptome and proteome regulation are driven by
different factors.
A k -means clustering of genes based on their mRNA or protein abundance
changes across LCLs.
B Median Pearson ’ s correlation coefficients (PCCs) for each transcriptome and
proteome k -means cluster. Genes assigned to different k -means clusters
can either be anti-regulated (e.g. T 1 and T 2 ) or not correlated (e.g. T 1 and
T 3 ). k -means clusters formed by genes that are co-regulated at the mRNA
level are not generally co-regulated at the protein level, and vice versa.
C Transcriptome clusters are strongly enriched for subcompartment A 1 or A 2 .
Dashed lines indicate the percentag e of genes expected if
subcompartments were evenly distributed across clusters.
D Proteome clusters are mainly composed of proteins fro m distinct
subcellular locations. Dashed lines indicate the percentage of genes
expected if subcellular locations were evenly distributed across clusters.
E Genomic and epige nomic features enriched in each cluster relative to the
whole dataset.
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Molecular Systems Biology Co-regulation of closeby genes Georg Kustatscher et al
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different genomic and epigenomic characteris tics. A1 and A2
subcompartments are both enriched for features associated with
transcriptionally active chromatin, but to different extents (Rao
et al , 2014). Interestingly, we also found clear differences in
histone modifications and DNA methylation associated with tran-
scriptome regulation groups (Fig 2E). For example, in comparison
with T2, T1 gene bodies are enriched for H3K9me3, depleted in
activating marks such as H3K4me3 and H3K27ac, are longer,
replicate later and have a lower GC content. These differences
mirror those observed between A2 and A1 subcompartm ents (Rao
et al , 2014). In contrast, T3 and T4 do not show these features
despite preferentially localising to A1 and A2 subcompa rtments.
Instead, T3 genes display heavy CpG methylation, which is almost
an order of magnitude stronger than for T4 genes. Consequently,
T3 and T4 define their own epigenetic subpopul ation within
A-type compartments.
Genes with co-regulated protein abundances are related
functionally, not spatially
Clustering analysis of protein expression profiles led to three
proteome regulation groups P1-3 (Fig 2A and Dataset EV3), explain-
ing more than 50% of the total variance (Appendix Fig S3). Neither
genome compartments nor epigenomic signatures appear to be asso-
ciated with proteome regulation groups (Fig 2C and E). In contrast,
proteome regulation groups broadly correspond to subcellular loca-
tions: nucleus (P1), mitochondria, ER and Golgi (P2) and cytoplasm
(P3) (Fig 2D). They are also enriched for biological processes taking
place in these subcellular locations (Appendix Fig S4). In contrast,
T1-4 only weakly coincide with subcellular locations or biological
processes.
Intriguingly, T1-4 and P1-3 are independent of each other, that is
genes that are clustered based on their transcript expression signa-
ture are generally not co-regulated on the protein level, and vice
versa (Fig 2B). This suggests that much of the mRNA coexpression
of genes from the same subcompartment may be non-functional.
Note that as for sequence proximity (see above), this appears to
contrast with a previous report that genes which are close in 3D
nuclear space often have similar functions (The
´ venin et al , 2014).
However, we also find significant enrichment of functional associa-
tions between genes from the same subcompartment (Fig EV2B).
Nevertheless, in quantitative terms, the extent of mRNA co-regula-
tion strongly exceeds co-function as well as protein co-regulat ion.
For example, while 11% of gene pairs in the same (intrachromoso-
mal) subcompartment have co-regulated mRNAs, < 1% have similar
functions according to STRING and are co-regulated at the protein
level (Fig EV2B).
Gene clustering within but not between chromosomes associates
with reduced expression noise
In yeast, clustering of genes in the genome sequence is associated
with reduced expression noise (Batada & Hurst, 2007; Wang et al ,
2011). However, the situation is more complex when considering
the 3D structure of the genome. Highly transcribed gene clusters
tend to form fewer contacts with other chromosomes, and genomic
loci with more interchromosomal contacts tend to have higher
expression noise (McCullagh et al , 2010; Sandhu, 2012).
We tested whether gene clustering has a similar effect in human
cells. For each gene in our dataset, we calculated a clustering
degree, defined as the average distance to its three nearest neigh-
bouring genes along the DNA sequence. We then compared the
expression noise of the 5% most and least clustered genes, respec-
tively. As observed in yeast, we find that gene expression noise in
LCLs is significantly reduced for genes in gene-dense areas (Fig 3A).
The noise-reducing effect is much more significant on the mRNA
than the protein level.
In a second step, we investigated whether gene clustering in
nuclear space has a similar noise-reducing effect. In principle, gene-
dense regions may interact with each other in 3D to benefit from
further noise reduction by forming “super-clusters”. The three
human histone gene clusters on chromosome 6, for example,
converge in 3D to form such a super-cluster (Sandhu et al , 2012).
Therefore, we calculated a second clustering degree for each gene,
defined as the average distance to its three nearest neighbours in
3D, using Hi-C contacts. To capture long-range interactions resulting
from chromosome folding, we only considered neighbouring genes
that were on the same chromosome, but at least 500 kb up- or
downstream in terms of DNA sequence. There is a positive correla-
tion between the clustering degree in 1D and 3D (PCC 0.32,
P < 6 × 10
 97
), suggesting that genes clustered along the sequence
are also more densely packed in the 3D structure of a chromosom e.
Moreover, this gene clustering due to chromosome folding is also
associated with a significant reduction of gene expression noise,
albeit not as strongly as sequence-based clusters (Fig 3A).
Next, we investigated clusters that genes from different chromo-
somes may form in nuclear space, calculatin g a third clustering
degree based on interchromosomal Hi-C contacts. As shown in yeast
(McCullagh et al , 2010; Sandhu, 2012), we find a negative correla-
tion between sequence-based and interchromosomal clustering
(PCC  0.1, P < 5 × 10
 11
). This suggests that gene-dense regions,
while forming long-range, noise-reducing interactions within the
same chromosome, are less likely to interact with gene clusters on a
different chromosome. Moreover, genes forming interchromosomal
clusters are associated with higher expression noise than those with
fewer interactions (Fig 3A). This difference is not statistically signifi-
cant but is in agreement with earlier findings in yeast (McCullagh
et al , 2010; Sandhu, 2012).
Coexpression of closeby genes is driven by stochastic epigenetic
fluctuations and regulatory interference
How can gene proximity lead to mRNA coexpression? Many inci-
dents of coexpressed genes that are close in sequence have been
linked to stochastic alternation between an active and inactive chro-
matin state (Batada et al , 2007). Such chromatin fluctuations can
lead to coordinated transcriptional bursts of all genes within a chro-
matin domain (Raj et al , 2006). We first compared the chromatin
environment of genes that are co-regulated with their sequence
neighbours with genes that show no such co-regulation (“neigh-
bours” being defined as genes whose TSSs are < 50 kb away). We
find that genes which are coexpressed with their neighbours are
more often flanked by heterochromatin, upstream of their transcrip -
tion start site (Fig 3B). This is consistent with mRNA coexpression
driven by stochastic spreading of the adjacent heterochrom atin
domain into the active locus, silencing all genes therein. This is
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Georg Kustatscher et al Co-regulati on of closeby genes Molecular Systems Biology
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reminiscent of subtelomeric regions in yeast, which are hot spots for
expression noise (Batada & Hurst, 2007) due to transient spreading
of telomeric heterochromatin (Anderson et al , 2014).
Notably, chromatin fluctuations may lead to mRNA coexpression
that is not restricted to genes in close spatial proximity. Chromatin
factors play a key role in creating gene expression noise (Newman
AB CD
EF G
H I J
Figure 3 . mRNA coexpression of neighbouring genes is driven by chromatin fluctuations and regulatory interference.
A Intrachrom osomal gene clustering reduces gene expression noise. We determined the expression noise (coefficient of variation, CV) of the most and least densely
clustered genes, considering three different types of clustering: in terms of sequence proximity (seq), using long-range Hi-C contacts ( > 500 kb) within the same
chromosome (intra) and using interchromosomal Hi-C contacts (inter). Expression noise is reduced for clustered genes, except for genes forming more
interchromosomal contacts (* P < 0 . 01 ,* * P < 0 . 002 , *** P < 5 × 10
 6
based on Kolmogorov – Smirnov test). Boxplot drawn in the style of Tukey, that is box limits
indicate the first and third quartiles, central lines the median, whiskers extend 1 . 5 times the int erquartile range from the box limits. Notches indicate the 95 %
confidence interval for compa ring medians.
B The upstream region of genes that are co-regulated with their neighbours, that is other genes within 50 kb, is more likely to be occupied by heterochromatin than
that of genes showing no such co-regulation. Heterochromatin regions in LCLs have been reported previously (Ernst et al , 2011 ).
C Epigene tic similarity calculated on the basis of histone marks and CpG methylation is a strong gene ral predictor of mRNA co-regulation. Curves are fitted to all
intrachromosomal gene pairs irrespective of their genomic distance .
D Two randomly picked gene pairs exemplifying low and high epigenetic simila rity, respectively. Each column represents a gene and each row an epigenetic feature.
Colours show the standardised, average abundance of each mark across the gene body.
E mRNA co-regulation requires epigenetic sim ilarity or spatial proximity, but not both. Intrachromosomal gene pairs were binned by epigenetic similarity and spatial
proximity (Hi-C contacts), and the percentage of co-regulated mRNAs is shown in colour. Note bins 2 and 4 are both enriched for co-regulated mRNAs despite
containing gene pairs that are spatially distant and epigenetically different, respectively.
F Description of bins highlighted in panel (E).
G Gene pairs binned as in (E) but colour showing percentage of co-regulated proteins. Protein co-regulation does not depend on epigenetic similarity or spatial
proximity.
H On averag e, gene pairs in bins 1 and 4 have many more Hi-C contacts than those in bins 2 and 3 , that is they are spatially closer. Dashed line shows average Hi-C
contacts between genes in the dataset.
I On average, gene pairs in bins 1 and 2 are epigenetically much more sim ilar than those in bins 3 and 4 . Dashed line shows average epigenetic similari ty between
genes in the dataset.
J Heterochromatin profile for genes in bins 1 – 4 .
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Molecular Systems Biology Co-regulation of closeby genes Georg Kustatscher et al
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et al , 2006). Fluctuating expression levels of, for example, a
histone-modifying enzyme may simultaneously affect all its target
chromatin domains in the genome. To test for such a global chro-
matin-mediated co-regulation effect, we determined the epigenetic
similarity between all genes in our dataset. We defined “epigenetic
similarity” based on the abundance of various histone marks within
gene bodies. We used the Mahalanobis distance to measure similar-
ity, as this takes into account that some histone marks are strongly
co-dependent, for example H3K9ac and H3K4me3. Genes with simi-
lar epigenetic profiles are targeted by a similar set of chromatin-
modifying factors, and are therefore expected to respond similarly
to stochastic fluctuations of these factors. Indeed, we find that the
epigenetic similarity is a strong predictor of non-functional mRNA
co-regulation (Fig 3C and D).
This chromatin fluctuation scenario is a passive mechanism
where genes simply respond to changes in their chromatin domain.
However, on a local scale, transcriptional changes of one gene may
directly affect the transcription of its neighbours, if chromatin
remodelling or transcription factors spill over to adjacent genomic
regions (Ebisuya et al , 2008; Wang et al , 2011). This “regulatory
interference” model crucially depends on spatial proximity, but
does not require co-regulat ed genes to be part of the same chro-
matin domain. To compare the impact of chromatin and gene
distance on non-functional mRNA coexpression, we grouped gene
pairs based on epigenetic similarity as well as based on Hi-C contact
frequency. We then observed which groups contain co-regulated
mRNAs (Fig 3E). This shows that gene pairs which are far apart
both spatially and epigenetically are rarely co-regulated (bin 3 in
Fig 3E and F). Gene pairs with similar histone marks tend to be
co-regulated, even if they are spatially distant (Fig 3E and H).
Co-regulation of such genes is consistent with the passive
chromatin fluctuation model, but not the transcriptional inter-
ference model. Importantly, spatially close gene pairs can be
co-regulated even if their histone marks show no similarity (bin 4
in Fig 3E and I). This type of coexpression is not consistent with
the passive chromatin fluctuation model, since the epigenetic dif-
ferences between the gene pairs suggest that, in steady state, they
occupy distinct chromatin domains. These genes are also the least
likely to be flanked by heterochromatin (Fig 3J). However, the
behaviour of gene pairs in bin 4 is consistent with the regulatory
interference model, where fluctuations in one gene affect the chro-
matin and transcriptional state of its neighbours, in sequence and
3D. Note that this effect is buffered at the protein level (Fig 3G),
which is in agreement with this type of coexpression being not
functional.
Buffering of non-functional mRNA coexpression tends to be a
non-selective process
Finally, we asked which post-transcriptional mechanisms might
buffer the coexpression of genes that are spatially close, but func-
tionally unrelated. In principle, this could be a selective process that
specifically targets closeby genes and disentangles their expression
patterns. Alternatively, buffering could be a neutral process, where
the lack of coordination between post-transcriptional mechanisms
prevents the mRNA coexpression to be propagated to the protein
level. In this case, a selective process would need to exist to ensure
that functionally related genes do in fact have co-regulated protein
abundances. To distinguish between these two possibilities, we
analysed five measures of post-transcriptional gene expression
control (Fig 4 ).
First, we tested whether gene pairs with sustained protein co-
regulation are more likely to have similar mRNA half-lives in LCLs
(Duan et al , 2013), relative to co-regulated gene pairs with buffered
protein abundances. Indeed, we find this to be the case, even
though the difference is modest (Fig 4A). Next, we analysed which
co-regulated gene pairs are more likely to be targeted by the same
miRNA (Helwak et al , 2013). Again, gene pairs that are also co-
regulated on the protein level are enriched for pairs sharing at least
one miRNA. Third, as an indication for translation-related effects,
we took into account ribosome profiling data for the LCL cell line
panel (Battle et al , 2015), which reflect both the abundance of
mRNAs and the extent to which they are occupied by ribosomes
(Ingolia, 2014). Gene pairs with coexpressed proteins are almost
three times as likely to have correlated ribosome profiles than pairs
which only have co-regulated mRNA abundance s. Then, we looked
at the impact of protein degradation, by considering the occurrence
of non-exponentially degraded proteins (NEDs) (McShane et al ,
2016). These are proteins that are rapidly degraded after synthesis,
for example because they are protein complex subunits produced
in super-stoichiometric amounts. Again, we find that NEDs are
enriched among gene pairs with co-regulated proteins rather than
those with buffered protein levels. Finally, we show that the
protein sequence length, which strongly correlates with the extent
of post-transcriptional control (Vogel et al , 2010), is more similar
for co-regulated than buffered proteins. Proximity in the genome
seemed to have no impact on the similarity of gene pairs in any of
the five measures of post-transcriptional gene expression control
investigated here (Fig 4B). Taken together, these results suggest
that buffering of co-regulated closeby genes may occur via a
neutral mechanism, with buffered gene pairs consistently lacking
the extent of shared post-transcriptional processing observed for
functionally related gene pairs. If mRNA coexpression is func-
tionally relevant, multiple layers of post-transcriptional control
appear to work together to ensure that this is propagated to the
protein level.
Discussion
Genes are not randomly distributed across the sequence and struc-
ture of the genome, forming clusters that tend to be coexpressed but
do not generally have a shared function. Gene expression noise is
detrimental to cell fitness, especially for housekeeping genes (Fraser
et al , 2004). Clusters of actively transcribed genes have low expres-
sion noise, which may drive the evolution of non-random gene
order (Batada & Hurst, 2007). The coexpression of functionally
unrelated neighbouring genes may then be a side effect of the selec-
tion for noise reduction. However, such coexpression is not neces-
sarily deleterious. As we show here, non-functional co-regulation is
frequently observed at the mRNA level, but is largely buffered at the
protein level. Consequently, non-functional coexpression is unlikely
to offset the benefit of noise reduction.
The expression profiles of genes in a cluster co-evolve, such that
the evolutionary change in expression of one gene on average
predicts changes in its neighbours (Ghanbarian & Hurst, 2015).
ª 2017 The Authors Molecula r Sys tems Biology 13 : 937 | 2017
Georg Kustatscher et al Co-regulati on of closeby genes Molecular Systems Biology
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Nevertheless, it is still unclear whether expression clusters are the
result of natural selection. In yeast, only the most highly coex-
pressed neighbours are conserved as a pair, but these also tend to
be functionally related (Hurst et al , 2002). Neighbouring gene pairs
that separate tend to show interchromosomal co-localisation (Dai
et al , 2014). In Drosophila , highly coexpressed neighbouring gene
pairs are less likely to be conserved than expected (Weber & Hurst,
2011). In mammals, although some coexpression clusters are evolu-
tionarily maintained (Se
´ mon & Duret, 2006), natural selection
generally tends to separate gene pairs that show a strong position-
related coexpression effect (Liao & Zhang, 2008) or that involve
tissue-specific expression (Lercher et al , 2002). This indicates that
non-functional coexpression can affect cell fitness under some
circumstances, possibly if it becomes so strong that it persists
through the uncoordinated post-transcriptional processes.
The existence of coexpression clusters may also reflect the way
new genes originate. For example, highly transcribed chromatin
regions are more susceptible to retroposition (Hurst et al , 2004).
Recently, it has been proposed that the large number of human gene
pairs in head-to-head orientation may arise from divergent tran-
scription of single genes, when initially noncoding, antisense tran-
scripts evolve into new protein-coding genes (Wu & Sharp, 2013). In
both of these cases, new genes would have no sequence homology
with their neighbours, and would therefore be unlikely to share
their function. However, some of the most well-known coexpression
clusters, such as histone gene clusters, arose by gene duplicat ion.
Gene duplicates could potentially explain why some gene clusters
are functionally related. There are 30 gene pairs in our dataset that
are located within 50 kb from each other and are coexpressed on
both the mRNA and the protein level. Of these, 10 (33%) are classi-
fied as paralogues by Ensembl, a strong enrichment considering that
paralogues account for only 1.5% of these closeby gene pairs over-
all. However, 20 (66%) of the clustered gene pairs with co-regulated
protein abundances show no evidence for paralogy, suggesting that
functionally relevant clusters need not necessarily arise by gene
duplication.
Our analysis focussed on housekeeping genes, because compara-
ble data for tissue- or condition-specific genes were not available.
Housekeeping genes constitute about half of all human genes (Uhle
´ n
et al , 2015). They have a higher tendency to cluster than other
genes (Lercher et al , 2002), presumably because they are more
sensitive to gene expression noise (Fraser et al , 2004). Interestingly ,
post-transcriptional expression control is particularly important for
housekeeping genes (Gandhi et al , 2011; Jovanovic et al , 2015).
Notably, transcriptional activation of induced genes can also lead to
co-activation of functionally unrelated neighbouring genes (Spitz
et al , 2003; Ebisuya et al , 2008). However, it remains to be seen if
such co-activation is also buffered at the protein level.
A
B
Figure 4 . Buffering of non-fun ctional mRNA co-regulation likely is a passive process.
A Percentage of gene pair s with coordinated post-transcriptional regulation, irrespective of genomic distance. Gene pairs with sustained protein co-regulation
consistently stand out as more likely to share similar aspects of post-transcriptional control. Genes were considered to have a similar mRNA half-life if the half-life
ratio between the more and less stable gene was < 1 . 5 . For miRNAs, all gene pairs targeted by at least one shared miRNA we re considered. Gene pairs were said to
have correlated ribosome profil es if their ribosome occupancy correlated with PCC > 0 . 5 (BH adj. P < 0 . 001 ) across LCLs. For the non-exponentially degr aded proteins
(NEDs) barchart, gene pairs cont aining at least one NED were counted. Coding length was considered sim ilar if the longer protein was < 1 . 5 -fold longer than the
shorter protein. Numbers of gene pairs are shown inside the bars. Statistical significance was calculated using Fisher ’ s exact test (* P < 0 . 01 ,* * P < 1 × 10
 6
,
*** P < 3 × 10
 27
).
B No striking relationship betwee n gene distance and the extent to which gene pairs show similar post-transcriptional regulation. Note that the small increase of
similar ribosome occupancy towards closeby genes may be explained by the fact that ribosome profiles partially re flect mRNA abundance.
Molecula r Sys tems Biology 13 : 937 | 2017 ª 2017 The Aut hors
Molecular Systems Biology Co-regulation of closeby genes Georg Kustatscher et al
8

In conclusion, non-functional mRNA coexpression, due to chro-
matin fluctuations and regulatory interference, is far more common
than previously thought. Generally, this does not hamper cell fitness
as post-transcriptional regulatory mechanisms enforce functional
coexpression while dampening non-functional coexpression. Our
observations suggest that evolution of human genome organisation
is driven by noise reduction, which is a hypothe sis initially made in
yeast (Batada & Hurst, 2007). The large presence of non-functional
coexpression of genes at the transcript but not protein level has
implications for the fields of transcrip tomics and proteomics when
screening for functional links between genes.
Materials and Methods
mRNA abundances in human lymphoblastoid cell lines
RNA-sequencing data for human lymphoblastoid cell lines (LCLs)
have been reported (Pickrell et al , 2010). Counts per mapped reads
were downloaded from http://eqtl.uchicago.edu and converted to
log2 “reads per kilobase transcript per million mapped reads”
(RPKMs). Genes expressed in < 30 LCLs were removed. In order to
make mRNA measurements comparable to proteomics data, expres-
sion levels needed to be analysed relative to the same reference
LCL. To do so, log2 RPKMs values from the reference cell line
GM19238 were subtracted from all other LCLs.
Protein abundances in human lymphoblastoid cell lines
Protein abundances in LCLs have also been reported (Battle et al ,
2015). They have been measured by mass spectrome try and quanti-
fied relative to the reference cell line GM19238, using stable isotope
labelling by amino acids in cell culture (SILAC) (Ong et al , 2002).
Mass spectrometry raw files were downloaded from the PRIDE
repository (Vizcaı
´ no et al , 2016) (project identifier PXD001406) and
re-processed using MaxQuant 1.5.2.8 (Cox & Mann, 2008). Raw files
tagged as “run2” were omitted. Mass spectra were searched against
human Swiss-Prot sequences downloaded from Uniprot (UniProt
Consortium, 2015). To facilitate combining mRNA and protein data-
sets, no protein isoforms were considered. We used non-normalised
SILAC ratios obtained by MaxQuant with at least two ratio counts.
Because the internal standard had been used as heavy SILAC sample,
heavy/light (H/L) SILAC ratios were inverted to obtain L/H ratios
(i.e. test LCLs / reference LCL). Proteins that could not be unambigu-
ously mapped to a single gene were removed, as were proteins
detected in 30 LCLs or less. SILAC ratios were also log2-transformed.
Combining mRNA and protein expression data
To combine mRNA and protein data, ENSEMBL gene IDs from RNA
sequencing were mapped to Uniprot IDs using Uniprot’s webtool
(UniProt Consortium, 2015). Genes with ambiguous mappings were
removed. We also only considered LCLs for which both mRNA and
protein data were available. The resulting file contains mRNA and
protein abundances for 4,188 human genes in 59 LCLs, relative to
the GM19238 reference sample (Dataset EV1). It contains 0.1 and
6.7% missing values for mRNA and protein measurements,
respectively.
Defining positions of genes in the genome
Genomic coordinates of human genes (dataset version GRCh38.p5)
were downloaded from ENSEMBL (Yates et al , 2016). As we are
considering genes but not specific transcript or protein isoforms,
transcription start sites (TSSs) were defined as the start site of the
outermost transcript of a gene.
Testing gene pairs for co-regulation
Coordinated up- and downregulation of gene expression was
measured using Pearson’s correlation coefficient (PCC). The gene
expression datasets for LCLs (Dataset EV1) were used as input. The
median log2 fold change of each LCL was set to zero, in order to
prevent correlations reflecting irrelevant data features such as
uneven mixing of light and heavy SILAC samples. Gene pairs
were considered to be co-regulated at PCC > 0.5, but only if the
correlation was significant (Benjamini and Hochberg-adjusted
P -values < 0.001).
Characterisation of genes as housekeeping genes
To demonstrate that the 4,188 genes in the LCL dataset belong to
the constitutively expressed core proteome, we performed a number
of tests:
Chromatin states of gene promoters
Chromatin states of the genome of the GM12878 lymphob lastoid
cell line were determined previously (Ernst et al , 2011). They
were downloaded as hg19 genome coordinates from the USCS
genome browser (Rosenbloom et al , 2015) and converted to
GRCh38 coordinates using the liftOver command line tool (avail-
able at https://genome-store.ucsc.edu/). Genomic regions with
conflicting chromatin state annotations, resulting from the genome
coordinates update, were removed. For each gene in our dataset,
the chromatin state mapping to its transcription start site was
determined.
GO term enrichment
A statistical overrepresentation test was performed using the
PANTHER classification system (Mi et al , 2016) according to the
reported protocol (Mi et al , 2013). Overrepresentation of Gene
Ontology Biological Process (slim) terms was assessed for our 4,188
genes compared to the entire human genome. Only significantly
enriched terms (more than twofold; P < 0.05 after Bonferroni
correction) were considered.
mRNA tissue expression data
mRNA expression levels in different human tissues have been
assessed using RNA sequencing (Uhle
´ n et al , 2015). Transcripts
detected with FPKM ≥ 1 were considered to be expressed.
Protein tissue expression data
Protein expression levels in different human tissues have been
assessed using mass spectrometry (Wilhelm et al , 2014) (available
at www.proteomicsdb.org). To avoid bias due to the incomplete
nature of current proteome maps, only tissues with expression
values for more than 6,000 proteins were considered.
ª 2017 The Authors Molecula r Sys tems Biology 13 : 937 | 2017
Georg Kustatscher et al Co-regulati on of closeby genes Molecular Systems Biology
9

Defining pairs of genes with related functions (focussed
on accuracy)
To test whether genes with related functions are co-regulated across
LCLs, we defined three sets of functionally linked gene pairs. Func-
tional associations in these test sets are as accurate — not as compre-
hensive — as possible.
Gene pairs from same protein complexes
Human protein – protein interaction pairs based on Reactome path-
ways (Fabregat et al , 2016) were downloaded from www.reactome.
org (homo_sapiens.interactions.txt file; March 2016). They were fil-
tered for physical interactions of the “direct_complex” category.
Gene pairs belonging to more than one complex and homodimeric
interactions were removed.
Gene pairs encoding enzymes from consecutive metabolic reactions
As for protein complexes, human protein – protein interaction pairs
based on Reactome pathways (Fabregat et al , 2016) were down-
loaded from www.reactome.org (homo_sapiens.interaction s.txt file;
March 2016). They were filtered for interactions of the “neighbour-
ing_reactions” category. These are interactions where one gene/
protein produces the input or catalyst for the second reaction. Any
gene pairs known to interact also physically, that is belonging to
the “direct_complex” or “indirect_complex” categories , were
removed. In addition, gene pairs were filtered for those involved
in metabolic pathways, as opposed to, for example, the cell cycle
pathway which would contain irrelevant reactions such as “Mis18
complex binds the centromere”. To do so, we first inferred all
pathways mapping to the metabolism root pathway, using the
pathway hierarchy relationship file (ReactomePathwaysRela-
tion.txt, available on www.reactome.org). Enzymatic reactions
belonging to each metabolic pathway were then identified using
another interaction file available from Reactome (homo_sapi-
ens.mitab.interactions.txt). Finally, to avoid “trivial” consecutive
reactions such as those involving ubiquitous metabolites like
NAD
+
, we removed metabolic reactions with more than ten neigh-
bouring reactions.
Gene pairs from identical subcellular locations
Subcellular localisations of human proteins were downloaded from
Uniprot (UniProt Consortium, 2015). Proteins localising to more
than one subcellular location were removed. To avoid trivial locali-
sations such as “cytoplasm”, only subcellular compartments with
200 or less known protein components were considered.
Defining pairs of genes with related functions (focussed
on completeness)
To estimate an upper limit for how many coexpressed neighbouring
genes may be functionally related, we defined a separate test set
based on the STRING database (Szklarczyk et al , 2017). Functional
associations in this test set are as comprehensive as possible.
Protein network data for Homo sapiens were downloaded from
http://string-db.org. We considered all functional associations with
a combined STRING score > 0.7. This score integrates various types
of evidence and indicates the likelihood of the association to be
biologically meaningful, specific and reproducible.
Testing functionally related gene pairs for co-regulation
Correlation coefficients were obtained for every gene pair in our
three test sets (protein complexes, consecutive metabolic reactions,
subcellular locations) and their distribution was displayed in histo-
grams. As a control, gene pairs were randomly shuffled to break the
link between the pairs. For example, gene pairs encoding subunits
of the same protein complexes were shuffled such that the same
genes were paired randomly, in which case most gene pairs encode
subunits of different protein complexes. The Kolmogorov – Smirnov
test was used to assess whether PCC distributions of relevant gene
pairs were significantly different from those obtained with rando-
mised pairs.
Chromosome co-regulation mapping
PCCs were calculated for all relevant gene combinations, as
described for histograms above. For chromosome co-regulation
curves, PCCs were plotted against the genomic distance between
transcription start sites, with curves fitted by a generalised additive
model. For chromosome co-regulation maps, genes were plotted in
their chromosomal order and PCCs between all gene combinations
were represented by a colour scale.
Hi-C interactions for our gene set
Hi-C contact matrices for a lymphoblastoid cell line (Rao et al ,
2014) were downloaded from NCBI GEO database (accession
GSE63525). An unpublished script from Liz Ing-Simmons (available
at https://github.com/liz-is/readhic) was adapted (available at
https://github.com/Rappsilbe r-Laboratory/readhic) and then used
to import the Hi-C contact matrices into R, using 10-kb resolution
and “KRnorm” normalisation for intrachromosom al pairs and 50-kb
resolution and “INTERKRnorm” normalisation for interchromoso -
mal pairs. All reads used passed the MAPQ > 0 filter. Hi-C data are
based on GRCh37 genome coordinates. GRCh37 transcription start
sites for all genes were obtained using the biomaRt R package
(Dur inc k et al , 20 09), co nsid erin g only th e TSS of th e outer most tr an-
sc rip t of ea ch ge ne. Th e Geno micI nter acti ons R pa ckag e (Ha rms ton
et al , 20 15 ) wa s used to de term ine th e co ntac t fr equen cy betw een th e
gen es in our data set, co nside ring th e me dian re ad coun t of al l Hi-C
pi xels in a ra nge  40 kb aro und th e TS S of each ge ne.
Analysis of genome subcompartments
Nuclear subcompartments A1, A2, B1, B2, B3 and B4 have been
defined previously (Rao et al , 2014). A genome-wide mapping of
subcompartments in a lymphoblastoid cell line is available via the
NCBI GEO database (accession GSE63525). Subcompartment anno-
tations were lifted from hg19/b37 to GRCh38 genome coordinates
using the UCSC genome browser service (Rosenbloom et al , 2015).
k -means clustering of transcript and protein expression changes
k -means clustering was performed using the default algorithm and
settings in R (R Core Team, 2016), with k = 4 (mRNAs) or k = 3
(proteins) and five random start sets. Values of k were chosen such
that the clusters explain at least 50% of the total variance.
Molecula r Systems Biolo gy 13 : 937 | 2017 ª 2017 The Aut hors
Molecular Systems Biology Co-regulation of closeby genes Georg Kustatscher et al
10

Analysis of cluster features
Subcellular locations
To get a broad understanding of subcellular locations enriched in
k -means clusters, we downloaded all Uniprot entries mapping to the
locations Nucleus (Uniprot subcellular location ID: SL-0191), Endo-
plasmic reticulum (SL-0095), Golgi apparatus (SL-0132), Mitochon-
drion (SL-0173) and Cytoplasm (SL-0086) (UniProt Consortium,
2015). Proteins localising to the Endoplasmic reticulum and/or the
Golgi apparatus were combined as “ER-Golgi”. Proteins mapping to
more than one organelle were removed.
GO term enrichment
A statistical overrepresentation test was performed using the
PANTHER classification system (Mi et al , 2016) according to the
reported protocol (Mi et al , 2013). Overrepresentation of Gene
Ontology Biological Process (complete) terms in each cluster, rela-
tive to other clusters, was assessed. Using PANTHER’s GO hierarchy
annotation, we reported only the most specific GO terms and omit-
ted any co-enriched parent terms for clarity. All reported GO terms
were significantly enriched ( P < 0.05 after Bonferroni correction).
Genomic and epigenomic features
Raw signals of ChIP-seq experiments for lymphoblastoid cells
were downloaded from ENCODE (ENCODE Project Consortium,
2012) in hg19 genomic coordinates. ENCODE accessions were
ENCFF000ARW (H2AZ), ENCFF000ARZ (H3K4me1), ENCFF000ATL
(H3K4me2), ENCFF001EXX (H3K4me3), ENCFF000ASJ (H3K27ac),
ENCFF000ATX (H3K79me2), ENCFF000AUF (H3K9ac), ENCFF000
AUL (H3K9me3), ENCFF000AUS (H4K20me1), ENCFF001EXC (H3K
27me3), ENCFF001EXP (H3K36me3), ENCFF001GNK (RepliSeq
G1b), ENCFF001GNN (RepliSeq G2), ENCFF001GNR (RepliSeq S1),
ENCFF001GNT (RepliSeq S2), ENCFF001GNX (RepliSeq S3) and
ENCFF001GOA (RepliSeq S4). These bigWig files were converted to
bedGraph files, lifted over to GRCh38 coordinates, cleared of any
resulting overlaps and converted back to bigWig files using
command line tools from the UCSC genome browser (Rosenbloom
et al , 2015) (tools available at https://genome-store.ucsc. edu/). GC
percentage over 5-bp windows was downloaded from the UCSC
genome browser (Rosenbloom et al , 2015). Average signals over
gene bodies were calculated with the UCSC bigWigAverageOverBed
command line utility, using the coordinates of our genes as bed
files. CpG methylation from reduced representation bisulphite
sequencing of a lymphoblastoid cell line was also available from
ENCODE (ENCODE Project Consortium, 2012) (experiment
ENCSR000DFT; file accession ENCFF001TLQ). After lifting the hg19
bedMethyl file over to GRCh38 genomic coordinates, the mean
percentage of CpG methylation in gene bodies was calculated using
an R script. For each epigenomic or genomic feature, the median
enrichment for genes in each k -means cluster, compared to all genes
in our dataset, was calculated and plotted as log2 ratio in a
heatmap.
Calculation of gene expression noise
Gene expression noise at the mRNA and protein levels was calcu-
lated as the coefficient of variation (CV; standard deviation divided
by the mean) of log2-transformed RPKM and SILAC ratios,
respectively. To avoid dividing by zero (for unchanged genes with a
log2 ratio of zero), a constant value of 10 was added to all mRNA
and protein log2 ratios before calculating the noise.
Calculating the clustering degree
To define local gene density in a manner that can be applied to both
the sequence and the 3D structure of the genome, we determined
the average distance of a gene to its three nearest neighbouring
genes. We calculated three such “clustering degrees” for each gene
in our dataset. For the sequence-based clustering degree, the
distance to neighbouring genes was calculated in base pairs. For
intrachromosomal clustering in 3D, gene distance was calculated
based on Hi-C counts. However, we only considered “nearest”
neighbours which were at least 500 kb away in terms of DNA
sequence, to catch long-range interactions and avoid replicating the
sequence-based clustering degree. For interchromosomal clustering,
we considered the three nearest neighbours on other chromosomes,
based on interchromosomal Hi-C contacts.
Heterochromatin profiles of upstream regions
Chromatin states throughout the LCL genome were previously
described (Ernst et al , 2011). To simplify the analysis, we combined
the five inactive chromatin states defined by Ernst et al (“Hete-
rochromatin”, “Repressed”, “Repetitive”, “Poised Promoter” and
“Insulator”) into one “heterochromatin” state. We then scanned the
promoter region of test genes for the presence of heterochroma tin,
moving in 100-bp intervals from  50,000 bp to + 10,000 bp relative
to their transcription start site.
Calculating epigenetic similarity
Epigenetic similarity was calculated on the basis of the histone mark
abundance within gene bodies (see section “Analysis of cluster
features” for processing of ChIP-seq data). For this analysis, we
considered H2AFZ, H3K4me1, H3K4me2, H3K4me3, H3K27ac,
H3K79me2, H3K9ac, H3K9me3, H4K20me1, H3K27me3, H3K36me3
and CpG methylation, but not GC content, gene length and replica-
tion timing. For every pair of genes, we then determined how simi-
lar or dissimilar they are regarding the abundance of these
epigenetic features. This was calculated using the Mahalanobis
distance measure, which takes into account that some histone
marks strongly covary.
Analysis of post-transcriptional mechanisms
mRNA half-lives in seven different LCLs were previously reported
(Duan et al , 2013). We first calculated the average half-life of each
mRNA in these LCLs. We considered two mRNAs to have a similar
stability if the half-life of the more stable one was < 1.5-fold longer
than the less stable one. mRNA targets of human miRNAs were also
described previously (Helwak et al , 2013). Ribosome occupancy
profiles for the LCL cell line panel were recently published (Battle
et al , 2015). We considered ribosome profiles for 57 LCLs and 4,033
genes for which we had matching mRNA and protein measure-
ments. We calculated Pearson correlation coefficients (PCCs) for
ribosome profiles between all gene pairs. Two genes were said to
ª 2017 The Authors Molecula r Systems Biolo gy 13 : 937 | 2017
Georg Kustatscher et al Co-regulati on of closeby genes Molecular Systems Biology
11

have correlated ribosome profiles at PCC > 0.5 (BH-adjusted P -
value < 0.001). Proteins subjected to non-exponential degradation
in human RPE-1 cells were also described recently (McShane et al ,
2016). Finally, protein sequence lengths were downloaded from
Uniprot (UniProt Consortium, 2015).
Human paralogous genes
Human gene duplicates were downloaded from ENSEMBL (Yates
et al , 2016). We only considered paralogues with at least 25%
sequence identity.
General data processing and plotting
Data processing was performed in R (R Core Team, 2016), unless
indicated otherwise. Plots were created using the ggplot2 package
(Wickham, 2009).
Expanded View for this article is available online.
Acknowledgements
This work was supported by the Wellcome Trust through a Senior Rese arch
Fellowship to JR (grant number 103139 ). The Wellcome Trust Centre for Cell
Biology is supported by co re funding from the Wellcome Trust (grant number
203149 ).
Author contributions
PG analysed Hi-C contact frequencies betw een the genes in our dataset. GK
and JR designed the study, analysed the data and wrote the paper.
Conflict of interest
The authors declare that they have no conflict of interest.
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Why institutions use Plag.ai for originality review, entry 69

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