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Kullback Leibler divergence in complete bacterial and phage genomes

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The amino acid content of the proteins encoded by a genome may predict the coding potential of that genome and may reflect lifestyle restrictions of the organism. Here, we calculated the Kullback–Leibler divergence from the mean amino acid content as a metric to compare the amino acid composition for a large set of bacterial and phage genome sequences. Using these data, we demonstrate that (i) there is a significant difference between amino acid utilization in different phylogenetic groups of bacteria and phages; (ii) many of the bacteria with the most skewed amino acid utilization profiles, or the bacteria that host phages with the most skewed profiles, are endosymbionts or parasites; (iii) the skews in the distribution are not restricted to certain metabolic processes but are common across all bacterial genomic subsystems; (iv) amino acid utilization profiles strongly correlate with GC content in bacterial genomes but very weakly correlate with the G+C percent in phage genomes. These findings might be exploited to distinguish coding from non-coding sequences in large data sets, such as metagenomic sequence libraries, to help in prioritizing subsequent analyses.
Content may be subject to copyright.
Submitted 30 June 2017
Accepted 22 October 2017
Published 30 November 2017
Corresponding authors
Ramy K. Aziz,
ramy.aziz@gmail.com
Robert A. Edwards,
redwards@mail.sdsu.edu,
raedwards@gmail.com
Academic editor
Ahmed Moustafa
Additional Information and
Declarations can be found on
page 14
DOI 10.7717/peerj.4026
Copyright
2017 Akhter et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Kullback Leibler divergence in complete
bacterial and phage genomes
Sajia Akhter1, Ramy K. Aziz2,3, Mona T. Kashef2, Eslam S. Ibrahim2,
Barbara Bailey4and Robert A. Edwards1,3,4,5
1Computational Science Research Center, San Diego State University, San Diego, CA, USA
2Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, Cairo, Egypt
3Department of Computer Science, San Diego State University, San Diego, CA, United States of America
4Department of Mathematics & Statistics, San Diego State University, San Diego, CA, USA
5Department of Biology, San Diego State University, San Diego, CA, USA
ABSTRACT
The amino acid content of the proteins encoded by a genome may predict the coding
potential of that genome and may reflect lifestyle restrictions of the organism. Here,
we calculated the Kullback–Leibler divergence from the mean amino acid content
as a metric to compare the amino acid composition for a large set of bacterial
and phage genome sequences. Using these data, we demonstrate that (i) there is a
significant difference between amino acid utilization in different phylogenetic groups
of bacteria and phages; (ii) many of the bacteria with the most skewed amino acid
utilization profiles, or the bacteria that host phages with the most skewed profiles,
are endosymbionts or parasites; (iii) the skews in the distribution are not restricted to
certain metabolic processes but are common across all bacterial genomic subsystems;
(iv) amino acid utilization profiles strongly correlate with GC content in bacterial
genomes but very weakly correlate with the G+C percent in phage genomes. These
findings might be exploited to distinguish coding from non-coding sequences in large
data sets, such as metagenomic sequence libraries, to help in prioritizing subsequent
analyses.
Subjects Bioinformatics, Computational Biology, Genomics, Microbiology, Statistics
Keywords Information theory, Metagenomics, Genomics, Genometrics
INTRODUCTION
The central dogma of molecular biology describes the irreversible flow of information
in biological systems from nucleic acids to amino acids, whose combinations make up
the main cellular components: proteins. In principle, such flow of information is no
different from other data storage and communication systems, and can thus be studied by
the information theory (Shannon, 1948). Indeed, the information theory has been often
applied to studying different aspects of prokaryotic and eukaryotic genomes, including,
for example, genome composition (Grigoriev, 1999;Omer, Harlow & Gogarten, 2017;Roten
et al., 2002), architecture (Dandekar et al., 1998;Koonin, 2009;Ochman & Davalos, 2006),
coding potential (Gerdol et al., 2015;Zeeberg, 2002), order and entropy (Bohlin et al., 2012;
Vinga, 2014), symmetry (Kong et al., 2009;Poptsova et al., 2009), and even the interaction
among genetic variants in comparative analysis.
How to cite this article Akhter et al. (2017), Kullback Leibler divergence in complete bacterial and phage genomes. PeerJ 5:e4026; DOI
10.7717/peerj.4026
Previous studies used Shannon’s index (Shannon, 1948) to classify informative DNA
sequences (Akhter et al., 2013;Chang et al., 2004;Chang et al., 2005;Chen et al., 2005) and
find prophages in bacterial genomes (Akhter, Aziz & Edwards, 2012). Shannon’s index
is now increasingly being used as a bioinformatics tool to solve problems related to
either network or genome context, e.g., comparative genomics, resolution-free metrics,
motif classification, and sequence-independent correlations (De Domenico & Biamonte,
2016;Vinga, 2014). Recently, a genome complexity metric was proposed, the biobit,
which balances a genome’s entropic and anti-entropic components (Bonnici & Manca,
2016). Additionally, von Neumann entropy, which originated from Shannon’s classical
information theory, is used as a divergence parameter that could be implemented from
spectral data to human microbiome networking (De Domenico & Biamonte, 2016).
In an attempt to prioritize analysis efforts for high-throughput sequencing data, notably
metagenomic data sets, we calculated the Shannon index of a representative sample of
bacterial and phage genomes, and showed that the information content of the nucleotide
sequence within a genome largely depends on the genome’s size and its GC content.
Subsequently, we were able to predict which sequences within a metagenomic library are
more likely to match sequences already deposited in public databases (Akhter et al., 2013).
In the current study, we continue to explore the usefulness of the information theory by
expanding our analysis to the coding potential of a genome, focusing on amino acids rather
than nucleotide content. To this end, we used the Kullback–Leibler divergence (KLD)
value (Kullback & Leibler, 1951) to examine biases in the amino acid composition of the
potentially translated gene products (predicted proteins) encoded by a genome.
Kullback & Leibler (1951) generalized Shannon’s approach to support statistical
comparisons between populations. The KLD value measures the deviation of one
distribution from another distribution. Here, we hypothesized that KLD might be a good
measure of the diversity of an encoded proteome. We demonstrate that KLD correlates well
with an organism’s phylogeny and amino acid utilization profile, in addition to correlating
with the GC content of bacterial genomes.
METHODS
Retrieval of sequence data
All genomic data, including gene annotations and functional classification, were obtained
from the public SEED database (http://pubseed.theseed.org (Aziz et al., 2012;Overbeek,
Disz & Stevens, 2004)). Complete phage genome sequences were obtained from the
Phantome database (http://phantome.org).
Calculation of Kullback–Leibler divergence
Kullback–Leibler divergence (KLD) was initially calculated for 372 whole bacterial genomes
and 835 complete phage genomes according to the following equation.
DKL(PkQ)=X
i
P(i)logP(i)/Q(i).
As used here, Piis the frequency of the ith amino acid in a given genome X, and Qiis
the average frequency of this amino acid calculated from all complete genomes, i.e., all
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 2/17
bacterial genomes were used for calculating Qiwhen the given genome Xis a bacterial
genome, all phage genomes were used for calculating Qiwhen Xis a phage.
The same strategy was followed for the calculation of KLD for a specific subsystem
(Overbeek et al., 2005); the subsystem analysis was conducted on subsystems covering 446
bacterial genomes. These were all bacterial genomes available at the time of the analysis,
with reliable subsystems coverage and a minimal set of informative metadata, in addition
to being known for hosting analyzed phages.
KLD for each phylogenetic class was calculated by the following equation, where n is the
number of genomes in each class.
1/n
n
X
j=1
X
i
P(i_j)log P(i_j)
Q(i_j).
RESULTS
Kullback–Leibler divergence in bacterial and phage genomes
KLD was calculated for all predicted proteins encoded by 372 bacterial genomes and 835
phage genomes. The skew in the KLD distribution, for all genomes combined, ranged from
0.002 to 0.22 (Fig. 1). We found that both the most skewed bacterial genome, Wigglesworthia
glossinidia (KLD =0.224), and the most skewed phage genome, Spiroplasma kunkelii virus
SkV1_CR2-3x (KLD =0.222), had a low GC content of 22%. We also found that phage
genomes have a slight tendency towards lower KLD values than bacterial genomes (Table 1).
This finding suggests that bacteria might have more biased amino acid utilization than
phages.
The phylogeny and lifestyle of the ten bacterial species and ten phages with the most
skewed amino acid composition (as measured by their KLD values) are shown in Tables 2
and 3, respectively. Consistently, a number of bacterial species whose genomes have
the most skewed amino acid compositions are parasites, and some of them are obligate
intracellular parasites—with a limited ecological niche range and restricted lifestyle (e.g.,
Wigglesworthia glossinidia, an endosymbiont of the tsetse fly, Table 2). Likewise, the
bacterial species that are hosts for the phage genomes with the most skewed amino acid
compositions are enriched in intracellular parasites (e.g., Spiroplasma kunkelii, a parasite
that causes Corn Stunt Disease, Table 3).
There are several possible explanations for these deviations in KLD. For example,
the variation could be phylogenetically biased or determined by the lifestyle of the
organism. Alternatively, a physical parameter such as the DNA composition, osmotic,
or thermodynamic stability might control the variation in production of amino acids and
composition of the proteins. The availability of amino acids, their precursors, or enzymatic
limits on the interconversion of amino acids may also affect these skews.
To investigate the impact of phylogeny on KLD deviations, we calculated the mean KLD
for each phylogenetic group and compared it to the mean KLD of all proteins. The variation
in amino acid composition provides a signature profile for each phylogenetic group (for
both phage and bacterial genomes, Fig. 2), which might be predictive for metagenomic
sequences.
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 3/17
Figure 1 Trends in amino acid composition divergence. (A) The 372 complete bacterial genomes
(black) and 835 complete phage genomes (blue) analyzed are ranked according to their composition. (B)
Box plots showing the amino acid composition divergence for bacteria (gray) and phages (blue).
Full-size DOI: 10.7717/peerj.4026/fig-1
GC content and amino acid variations among genomes
Amino acid deviation between different phages and bacteria were compared (Figs. 1 and 2).
To inspect the functional significance of those differences, we compared the composition of
proteins involved in different aspects of metabolism. In this comparison, the null hypothesis
was that the compositional bias was randomly distributed among all protein metabolic
functional classes, and the alternative hypothesis was that the bias was limited to one or
a few functional groups that might contain critically skewed amino acid compositions in
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 4/17
Table 1 Percentage of phage and bacterial genomes in different range of KLD value.
KLD categories Bacteria
(total 372 genomes)
Phage
(total 835 genomes)
KLD > 0.1 6.7% (25 genomes) 2% (18 genomes)
KLD > 0.05 25.8% (96 genomes) 19% (160 genomes)
KLD > 0.025 56% (210 genomes) 52% (435 genomes)
Table 2 The most skewed bacterial genomes.
Genus and Species KLD of amino
acid composition
from the mean
%GC Comments
Wigglesworthia glossinidia endosymbiont of Glossina
brevipalpis
0.224 22.5 Wigglesworthia are obligate intracellular bacteria and
endosymbionts of the tsetse fly
Mycoplasma mobile 163K 0.162 25.0 Fish pathogen
Mycoplasma mycoides subsp. mycoides SC str. PG1 0.162 24.0 Cattle pathogen
Borrelia burgdorferi B31 0.162 28.2 A human pathogen that lives in rodents, and can be
transferred to humans via tick bites.
Borrelia garinii PBi 0.162 28.1 A human pathogen that lives in rodents, and can be
transferred to humans via tick bites.
Mycoplasma hyopneumoniae 232 0.156 28.6 Pig pathogen responsible for porcine pneumonia
Ureaplasma parvum serovar 3 ATCC 700970 0.155 25.5 Mucosal pathogen of humans
Mycoplasma hyopneumoniae 7448 0.154 28.5 Pig pathogen responsible for porcine pneumonia
Mycoplasma pulmonis UAB CTIP 0.154 26.6 Mouse pathogen causing murine pneumonia
Mycoplasma hyopneumoniae J 0.153 28.5 Pig pathogen causing porcine pneumonia
Table 3 The most skewed phage genomes.
Virus type KLD of amino
acid composition
from the mean
%GC Comments on the host
Spiroplasma kunkelii virus SkV1_CR2-3x 0.222 22.2 Parasitic lifestyle. Causative agent of Corn Stunt Disease
Spiroplasma phage SVTS2 0.211 22.7 Parasitic lifestyle
Spiroplasma phage 1-C74 0.199 23.1 Parasitic lifestyle
Propionibacterium phage B5 0.192 64.3 A parasite and commensal of humans and other animals
that lives in and around sweat glands, sebaceous glands and
other areas of the skin (Lood & Collin, 2011)
Spiroplasma phage 1-R8A2B 0.186 22.9 Parasitic lifestyle
Acholeplasma phage MV-L1 0.183 33.3 N/Aa
Mycoplasma phage phiMFV1 0.167 25.1 Parasitic lifestyle
Clostridium phage D-1873 CLG.Contig168 0.153 25.3 N/Aa
Mycoplasma phage P1 0.152 26.8 Parasitic lifestyle
Clostridium phage c-st 0.133 26.3 N/Aa
Notes.
aN/A, No metadata available on pathogenesis or lifestyle of the host.
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 5/17
Figure 2 Amino acid divergence varies for each phylogenetic taxon of bacteria and phage bacterial
hosts. The divergence of amino acid composition for each phyogenetic group from the mean of all bacte-
ria and phages is shown. Error bars represent the standard error of the mean. The numbers represent the
number of genomes considered for each class.
Full-size DOI: 10.7717/peerj.4026/fig-2
some genome. To address this potential source of bias, we used SEED subsystems (Overbeek
et al., 2005), collections of genes in pathways or functional associations manually curated
by teams of annotators in the SEED database (Aziz, 2010;Aziz et al., 2012). Different
subsystems are arranged in a hierarchy of groups.
Bacterial genomes
At the time this study was performed there were 31 top-level classifications for protein
functions, 229 first–second level classifications (the second level is not unique, but the
combination of first and second level is), and 1,078 third level classifications (the subsystems
themselves). To investigate whether the amino acid skews in protein composition are
dependent on protein function, we calculated KLD for each subsystem’s first level hierarchy
in ten bacterial genomes. Five were chosen from the most extremely skewed organisms
(Table 2), and five were chosen at random from the remaining genomes. KLD values of the
five bacterial species with most skewed amino acid composition greatly differed from the
mean for all subsystems, as expected from their overall bias. However, those differences
were not restricted to one or a few metabolic process, but were rather consistent across all
subsystems tested (Fig. S1), so the hypothesis that the distribution of skewed amino acids
is non-random across the genome or that it is dependent of functional categories could
not be confirmed. The five control bacterial species, chosen at random, exhibited much
less variation in amino acid composition (Fig. 3).
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 6/17
Figure 3 Divergence of amino acid composition and phylogeny. Comparison of the divergence of
amino acid composition and phylogenetic group for the most divergent bacterial genomes (A) and the
genomes of five bacteria chosen at random (B). In (A) the first five bars are Wigglesworthia glossinidia,
Borrelia garinii, Mycoplasma mycoides, Ureaplasma parvum serovar and Buchnera aphidicola (see Table 2).
In (B) the first five bars are Bifidobacterium adolescentis,Bacillus B-14905,Nostoc sp. PCC 7120,Salmonella
bongori 12149, and Chlamydophila pneumoniae CWL029. In both panels, the sixth bar is for the mean of
amino acid utilization for each subsystem. (Note the difference in y-axis scale between the two panels).
Full-size DOI: 10.7717/peerj.4026/fig-3
To examine whether the compositional skew of bacterial protein sequences was only
restricted to one or a few amino acids, we calculated the frequency of occurrence of
each amino acid for the five bacterial genomes that have the most skewed amino acid
composition (Fig. 4). The null hypothesis was that there would be random changes in the
amino acid compositions in these genomes. However, the initial hypothesis was rejected:
all five bacterial genomes were found to have significantly reduced their utilization of the
amino acids alanine (A), glycine (G), proline (P), and arginine (R), compared to the mean
amino acid utilization calculated from all bacteria. This utilization bias appears to have
been compensated by an increase in the utilization of the amino acids isoleucine (I), lysine
(K) and asparagine (N).
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 7/17
Figure 4 Frequency of each of the twenty amino acids in the three domains of life and the most skewed
genomes. The first three bars are the average frequency of amino acid for the three domains Archaea, Bac-
teria and Eukaryota. The next five bars are for Ureaplasma parvum serovar, Wigglesworthia glossinidia,
Borrelia garinii, Buchnera aphidicola, and Mycoplasma mycoides. Arrows indicate amino acid frequency
smaller or larger than the mean for these five bacteria.
Full-size DOI: 10.7717/peerj.4026/fig-4
This switch in amino acid utilization has a considerable biological impact because these
amino acids are discriminatory in the standard genetic code. A genome consisting entirely
of guanosine and cytosine could only encode for alanine (GCC or GCG), glycine (GGC
or GGG), proline (CCC or CCG), or arginine (CGC or CGG). In contrast, a genome that
contains only adenosine and thymidine could only encode for asparagine (AAT), isoleucine
(ATT or ATA), leucine (TTA), lysine (AAA), phenylalanine (TTT), or tyrosine (TAT).
Thus, the skew in amino acid composition seems to have been driven by the GC content
of the DNA sequence more than the lifestyle, phylogeny, or other characteristics associated
with the genome.
The correlation between the percent of sequences that are either guanosine or cytosine
(%GC) and the KLD of the amino acid composition to the mean was calculated (Fig. 5).
The relationship between %GC and amino acid divergence is given by the equation
y=2(x0.5)2, where xis the %GC and y is the divergence of amino acid composition
(with a square of correlation coefficient, of 0.84). To test whether the correlation is similar
for all areas of metabolism, the relationship between %GC and KLD was calculated for the
different subsystems shown in Fig. 4. Most subsystems had similar parabolas suggesting that
the DNA content and amino acid composition were related. However, the relationship did
not hold for the secondary metabolism subsystems (the square of correlation coefficient fell
to 0.119, Fig. 5). This suggests that the amino acid profiles of proteins involved in secondary
metabolism subsystems are not related to the GC content of the genome. We hypothesize
that this may imply that genomic subsystems involved in secondary metabolism are more
frequently horizontally transferred than those involved in core metabolism, which are
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 8/17
Figure 5 Comparison of KLD and GC-content for all bacterial genomes, and for individual groups
of subsystems. The GC content of each genome is plotted on the x-axis, and the variation in amino acid
composition is shown on the y-axis.
Full-size DOI: 10.7717/peerj.4026/fig-5
usually highly conserved, and we may be observing the skew of the donor organism rather
than the current host.
Phage genomes
To examine the amino acid utilization behavior for the most skewed phage genomes,
we analyzed three GC-rich and three GC-poor phage genomes in more detail. Similar to
bacterial genomes, the amino acid composition also seems to be driven by the GC content
for the most skewed phages (Fig. 6). For example, for amino acids lysine (AAA, AAG) and
isoleucine (ATT, ATC, ATA), phage genomes with poor GC content have higher frequency
but the GC-rich phage genomes have relatively lower frequency compared to the average
amino acid utilization among 835 phage genomes.
Like with bacterial genomes, deviation of the amino acid composition (KLD) in phage
genomes strongly correlates with their GC% (Fig. 7A). The relationship is y=1.7(x0.5)2,
where xis the %GC and yis the KLD (with a square of correlation coefficient of 0.84). The
relationship between KLD and GC content is statistically different for bacteria and phages
(p-value < 106, details in Supplemental Information). To check whether the variation
of amino acid utilization is restricted to one or a few subsystems, KLD was calculated
for several phage subsystems in all phage genomes. No strong correlation was observed
between functional category and GC%, with the exception of the phage replication
subsystem (correlation coefficient, R2=0.3). This lack of correlation can be explained by
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 9/17
Figure 6 Amino acid frequency in phage genomes. The first three bars are for Spiroplasma kunkelii virus
SkV1_CR2-3x (GC =22%), Mycoplasma phage phiMFV1 (GC =25%) and Sulfolobus islandicus rod-
shaped virus 1 (GC =25%). These three genomes are GC poor genomes. The fourth bar represents the av-
erage frequency of amino acid for 835 phage genomes. The last three bars are for Propionibacterium phage
B5 (GC =64%), Thermus phage P23-77 (GC =67%) and Streptomyces phage VWB (GC =71%), which
are GC rich genomes.
Full-size DOI: 10.7717/peerj.4026/fig-6
the highly diverse nature of phages, which have different mutational and gene transfer
dynamics than bacteria.
GC content and amino acid frequency within genomes
As the variation of amino acid in a genome (for both bacteria and phages) has a strong
correlation with the genome’s GC content, the frequency of amino acid utilization was
calculated and plotted against %GC for 446 bacterial genomes and 835 phage genomes
(Figs. S2,S3). The correlation between each amino acid and %GC for both phages and
bacterial genome follows a similar pattern, although, for phages, there is almost no
correlation between the amino acid deviation and %GC for most subsystems.
DISCUSSION
A complete genome, unlike random sequences, represents an evolutionary successful set
of nucleotides, whose combination encodes a functioning organism that survived selection
pressure through the evolutionary times and that is still evolvable (Hogeweg, 2012). The
accrual of complete genome sequences provides an invaluable resource for exploring the
different means by which the combination of four nucleotides (A, G, C, and T/U) encodes
life forms able to survive the different environments of our planet. Because the genetic code
is relatively simple yet redundant, studying the information content inherent to complete
genome sequences is expected to enable the discovery of various properties of a genome’s
architecture (e.g., gene order and density, and genome symmetry), compositional bias
(e.g., GC content and skews), coding potential (i.e., all possible amino acid combinations
it can encode), codon usage preferences, and epistatic parameters. Such properties can
be correlated with functional aspects encoded by the genome and can shed the light
on its natural history, allowing the study of the organism’s evolution (Adami, 2012;
Gautier, 2000;Nasrallah & Huelsenbeck, 2013). For example, epistatic parameters and their
statistical analysis gave clues on the evolution of influenza A virus (Nshogozabahizi, Dench
& Aris-Brosou, 2017).
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 10/17
Figure 7 Comparison of KLD and GC-content for all phage genomes. (A), and for individual groups of
subsystems (B–H). The GC content of each genome is plotted on the x-axis, and the variation in amino
acid composition is shown on the y-axis. The correlation equation for A–complete phage genomes is y=
1.7x21.7x+0.44, and the correlation coefficient R2=0.63. In (B), the skews are only shown for those
proteins in the phage replication subsystems, and the equation is 2.2x22.3x+0.6, with a correlation co-
efficient =0.3. In (C), the skews are only shown for those proteins involved in the capsid subsystems, and
the representative equation is 0.34x20.26x+0.1, with a correlation coefficient =0.025. In (D), the skews
are only shown for those proteins involved in phage head, and the equation is 0.739x20.79x+0.27, with
a correlation coefficient =0.029. In (E), the skews are only shown for proteins in subsystems involved in
phage lysis, and the equation is 0.31x2+0.37x0.189, (continued on next page...)
Full-size DOI: 10.7717/peerj.4026/fig-7
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 11/17
Figure 7 (... continued)
with a correlation coefficient =0.01. In (F), the skews are only shown for those proteins in experimental
subsystems (mostly uncharacterized proteins), and the equation is 0.65x20.7x+0.26, with a correlation
coefficient =0.01. In (G), the skews are only shown for those proteins involved in phage tail subsystems,
and the equation is 0.285x20.4x+0.1856, with a correlation coefficient =0.095. In (H), the skews are
only shown for those proteins in phage tail fiber subsystems, and the equation is 3.2x23.09x+0.79, with
a correlation coefficient =0.15.
Additionally, these compositional and informational properties can be exploited to
develop better strategies of genome interpretation. For example, the information theory,
compositional statistics, and genome topography have been extensively used in gene
prediction, genome assembly, RNA finding (Bernhart & Hofacker, 2009;Li et al., 2010),
and the prediction of horizontal gene transfer (Davis & Olsen, 2010;Langille & Brinkman,
2009;Mrazek & Karlin, 1999;Ochman, Lawrence & Groisman, 2000;Price, Dehal & Arkin,
2008). Lately, more sophisticated analyses aimed at differentiating between informative
and less informative sequences in viral genome analyses and metaviromics (Watkins &
Putonti, 2017).
In this study, we explored the possibility of exploiting the coding potential and amino
acid distribution biases within complete bacterial and phage genomes for better interpreting
sequence fragments (e.g., metagenomic reads), and predicting which sequence reads within
large data sets are likely to encode proteins. To this end, we calculated KLD, a measure
of information divergence, for a set of bacterial and phage genomes, and compared the
distribution of amino acids in different protein-coding sequences in an attempt to use this
metric as a measure of how much those sequences deviate from the standard—the standard
being defined by the combined amino acid distribution in all genomes.
We found a significant difference in amino acid utilization between phylogenetic groups
of bacteria and phages. In addition, we found an enrichment of intracellular endosymbiotic
or pathogenic bacterial genomes among those with the most skewed amino acid utilization
profiles, or an enrichment of phages that infect such bacteria. Whereas amino acid skews
did not seem to be restricted to a particular functional subsystem, they strongly correlated
with the GC content of bacterial genomes (Salzberg et al., 1998;Kelley et al., 2012).
Many studies have shown that the GC content of a genome influences the frequencies
of oligonucleotides and thus amino acid composition of its encoded proteome, which
reflect the lifestyle of the organism (e.g., Bharanidharan et al., 2004;Bohlin, Skjerve &
Ussery, 2008;Lobry, 1997;Najafabadi & Goodarzi, 2004;Rocha & Danchin, 2002;Ren et
al., 2017). It is also correlated with the GC proportion of all the synonymous codons for
a particular amino acid and has an impact on codon/amino acid usage (Davis & Olsen,
2010;Gerdol et al., 2015). In this work, we demonstrate how the GC content is driving
the divergence of amino acid composition in bacterial genomes away from the mean
composition through the use of KLD divergence. All five bacterial genomes with the
highest amino acid compositional skew have low GC content (ranging from 22% to 28%),
and consequently fewer alanine, glycine, proline and arginine residues in their encoded
proteins. Their relative inability to encode these amino acids, and their substitution of them
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 12/17
with isoleucine, lysine, and asparagine explains the significant skews seen in the protein
sequences (Fig. 3).
Conversely, GC-rich bacteria have fewer codons for phenylalanine, isoleucine, lysine,
asparagine, and tyrosine, but compensate with alanine, glycine, proline, and arginine.
Therefore, both GC-rich and GC-poor bacteria have the most divergent amino acid
compositions, while bacteria with an average GC content have an average amino acid
composition (Fig. 4). The correlation coefficient (R2=0.84) suggests a strong relationship
between GC% and KLD. However, as the relationship is not linear, we propose that this
relationship gives a better understanding of the correlation between GC content and the
variation of amino acid utilization.
The divergence of amino acid composition is not restricted to one or a few functional
categories, but is common across all subsystems. For almost all subsystems involved in
primary metabolism, the relationship closely follows similar quadratic equations with high
correlation coefficients. In contrast, subsystems involved in secondary metabolism appear
to have a poor correlation between GC content and amino acid composition. Two possible
reasons for this are a high level of horizontal gene transfer in genes within these subsystems,
ameliorating the amino acid utilization, or the poorer quality of annotation of secondary
metabolism in diverse organisms. Only 167 bacteria have an annotated secondary metabolic
subsystem, and most of those have GC content between 40% and 60%.
Some differences were noted in the trends of KLD variation between bacterial and
phage genomes. Phages have slightly lower KLD than bacterial genomes, albeit not strongly
statistically significant, which suggests that bacterial genomes may have more homogeneous
amino acid frequencies than phage genomes. This could be because bacterial genomes are
more conserved than those of phages, which could be the result of strong negative selection
pressure exerted on core metabolic and information transfer subsystems in bacteria, as
opposed to the lack of core sets of genes among known phages. Moreover, phage population
dynamics, their mode of replication, and their rapid turn over result in highly variable,
mosaic genomes.
It is worth noting that many of the phages with most skewed amino acid composition
infect bacterial endosymbionts or obligate parasites. This observation is consistent with
our hypothesis that KLD values reflect genome conservation, a phenomenon exaggerated
in genomes with limited environment and poor exchange with other sources of DNA.
Endosymbionts and intracellular parasites are confined to closed environments, and thus
their genomes have the highest variation from the average amino acid distribution. On the
other hand, genomes of bacteria (or phages that infect them) living in open environments,
which are continuously changing have less variation from the average distribution.
Additional evidence for this correlation between KLD and sequence conservation comes
from the observation that only within the ‘‘phage replication’’ subsystem, the correlation
between KLD and GC content is strong: phage replication genes are among the very few
genes that are conserved across most phage types.
Interestingly, Mycoplasma species, which are known to be intracellular parasites, were
the only bacterial species with the most skewed KLD values (Table 2) and which host phage
genomes with the highest KLD skew as well (Table 3). In a recent study, where the KLD
Akhter et al. (2017), PeerJ , DOI 10.7717/peerj.4026 13/17
was calculated for tetranucleotides in bacterial genomes (Bohlin et al., 2012), Mycoplasma
sp. was also considered as the most skewed bacterial genome.
ACKNOWLEDGEMENTS
We thank Peter Salamon for his insightful suggestions and comments.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This work was supported by the PhAnToMe grant from the National Science Foundation
(NSF) Division of Biological Infrastructure (DBI-0850356 to Robert A. Edwards), which
also partly covered Sajia Akhter and Ramy K. Aziz while at SDSU. Robert A. Edwards is
also supported by NSF grant MCB-1330800. Ramy K. Aziz is partly funded by Faculty of
Pharmacy, Cairo University, Grant IRG-2015-2. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
National Science Foundation (NSF) Division of Biological Infrastructure: DBI-0850356,
MCB-1330800.
Faculty of Pharmacy, Cairo University: IRG-2015-2.
Competing Interests
Ramy K. Aziz is an Academic Editor for PeerJ, but has no involvement in the peer review
or decision-making regarding the publication of this manuscript.
Author Contributions
Sajia Akhter conceived and designed the experiments, performed the experiments,
analyzed the data, wrote the paper, prepared figures and/or tables.
Ramy K. Aziz conceived and designed the experiments, analyzed the data, wrote the
paper, reviewed drafts of the paper.
Mona T. Kashef analyzed the data, wrote the paper, reviewed drafts of the paper.
Eslam S. Ibrahim analyzed the data, reviewed drafts of the paper.
Barbara Bailey analyzed the data, contributed reagents/materials/analysis tools, statistical
analysis.
Robert A. Edwards conceived and designed the experiments, analyzed the data, wrote
the paper, prepared figures and/or tables, reviewed drafts of the paper.
Data Availability
The following information was supplied regarding data availability:
The raw data has been provided in Supplemental Files.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.4026#supplemental-information.
Akhter et al. (2017), PeerJ, DOI 10.7717/peerj.4026 14/17
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... Biological sequences are commonly analyzed using informational entropy-based quantitative methods, e.g. Shannon entropy, mutual information, Kullback-Leibler divergence, Renyi entropy, diffusion entropy, topological entropy, etc., and their estimators [8][9][10][11][12][13][14][15][16][17]. The Shannon entropy [1] of nucleotide distributions has long been studied as a predictor of genomic sequence functionality and taxonomy [18][19][20][21][22]. Similarly, the distributions of DNA words (i.e. ...
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