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\begin{document}

\title{In-depth analysis of amino acid and nucleotide sequences of Hsp60: how
conserved is this protein?}



\author[1]{Tatyana Tikhomirova}%
\author[2]{Maxim Matyunin}%
\author[3]{Mikhail  Lobanov}%
\author[3]{Oxana Galzitskaya}%
\affil[1]{1Institute for Biological Instrumentation, Federal Research Center “Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences”}%
\affil[2]{3	Institute of Protein Research, Russian Academy of Sciences}%
\affil[3]{Institute of Protein Research}%


\vspace{-1em}



  
  \date{May 28, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Chaperonin Hsp60, as a protein found in all organisms, is of great
interest in medicine, since it is present in many tissues and can be
used both as a drug and as an object of targeted therapy. Hence, Hsp60
deserves a fundamental comparative analysis to assess its evolutionary
characteristics. It was found that the percent identity of Hsp60 amino
acid sequences both within and between phyla was not high enough to
identify Hsp60s as highly conserved proteins. In turn, their amino acid
composition remained relatively constant. At the same time, the analysis
of the nucleotide sequences showed that GC content in the Hsp60 genes
was comparable to or greater than the genomic values, which may indicate
a high resistance to mutations due to tight control of the nucleotide
composition by DNA repair systems. Natural selection plays a dominant
role in the evolution of Hsp60 genes. The degree of mutational pressure
affecting the Hsp60 genes is quite low, and its direction does not
depend on taxonomy. Interestingly, for the Hsp60 genes from Chordata,
Arthropoda, and Proteobacteria the exact direction of mutational
pressure could not be determined. However, upon further division into
classes, it was found that the direction of the mutational pressure for
Hsp60 genes from Fish differs from that for other chordates. The
direction of the mutational pressure affects the synonymous codon usage
bias. The number of high and low represented codons increases with
increasing GC content, which can improve codon usage.%
\end{abstract}%



\sloppy


\textbf{In-depth analysis of amino acid and nucleotide sequences of
Hsp60: how conserved is this protein?}

Tatyana S. Tikhomirova\selectlanguage{ngerman}\textsuperscript{1¶}, Maxim A.
Matyunin\textsuperscript{2¶}, Michail Yu. Lobanov\textsuperscript{2} and
Oxana V. Galzitskaya\textsuperscript{2,3*¶}

\textsuperscript{1}Institute for Biological Instrumentation, Federal
Research Center ``Pushchino Scientific Center for Biological Research of
the Russian Academy of Sciences'', Pushchino, Moscow Region, Russia

\textsuperscript{2}Institute of Protein Research, Russian Academy of
Sciences, Pushchino, Moscow Region, Russia.

\textsuperscript{3}Institute of Theoretical and Experimental Biophysics,
Russian Academy of Sciences, Pushchino, Moscow Region, Russia

*Corresponding author

E-mail: ogalzit@vega.protres.ru (O.V.G.)

\textbf{Abstract}

Chaperonin Hsp60, as a protein found in all organisms, is of great
interest in medicine, since it is present in many tissues and can be
used both as a drug and as an object of targeted therapy. Hence, Hsp60
deserves a fundamental comparative analysis to assess its evolutionary
characteristics. It was found that the percent identity of Hsp60 amino
acid sequences both within and between phyla was not high enough to
identify Hsp60s as highly conserved proteins. In turn, their amino acid
composition remained relatively constant. At the same time, the analysis
of the nucleotide sequences showed that GC content in the Hsp60 genes
was comparable to or greater than the genomic values, which may indicate
a high resistance to mutations due to tight control of the nucleotide
composition by DNA repair systems. Natural selection plays a dominant
role in the evolution of Hsp60 genes. The degree of mutational pressure
affecting the Hsp60 genes is quite low, and its direction does not
depend on taxonomy. Interestingly, for the Hsp60 genes from Chordata,
Arthropoda, and Proteobacteria the exact direction of mutational
pressure could not be determined. However, upon further division into
classes, it was found that the direction of the mutational pressure for
Hsp60 genes from Fish differs from that for other chordates. The
direction of the mutational pressure affects the synonymous codon usage
bias. The number of high and low represented codons increases with
increasing GC content, which can improve codon usage.

\textbf{Keywords:} chaperonin, bioinformatics, codon usage bias, amino
acid compositions, database construction

\textbf{Abbreviations:} IL-8 (6), interleukin-8 (6); IgG (A),
immunoglobulin G (A); Hsp60, heat shock protein (60 kDa); PID, percent
identity; UPGMA, unweighted pair group method with arithmetic mean; ENC,
effective number of codons; RSCU, relative synonymous codon usage.



\textbf{Introduction}

Hsp60 (\textasciitilde{}60 kDa) is a member of the heat shock protein
family. The main function of Hsp60 is to capture newly synthesized or
denatured protein and promote its folding. It should be noted that Hsp60
exhibits the properties of a molecular chaperonin not only at high
temperatures, as its name suggests, but also under conditions of
moderately acidic\textsuperscript{1} or high
salinity\textsuperscript{2}. Because the cells of organisms are under
incessant stress, Hsp60 is a highly expressed protein. Thus, its
ubiquitous distribution makes Hsp60 a promising object for research and
use.

Today Hsp60 plays an important role in medicine. In particular, Hsp60,
which is involved in the pro-inflammatory response, up-regulates the
production of IL-8\textsuperscript{3} and IL-6\textsuperscript{4} in
human bronchial epithelial cells and microglia, respectively. In
addition, Hsp60 as an adipokine can be released from adipose
tissue\textsuperscript{5}. The level of chaperonins in the blood of
obese patients positively correlates with markers of inflammation, which
may indicate the development of cardiovascular disease. Hsp60 is also
involved in the autoimmune response as an antigen\textsuperscript{6}.
Since Hsp60 is overexpressed in tumor cells, it can be used both to
detect the early stage of cancer\textsuperscript{7} and to develop
immunogenicity against it\textsuperscript{8}. On the other hand,
chaperonin can be used as a target not only for immunotherapy, but also
for antibiotic therapy of bacterial diseases such as African sleeping
sickness\textsuperscript{9,10} and \emph{Mycobacterium
tuberculosis}infections\textsuperscript{11}. In such cases, Hsp60 of
pathogenic microorganisms is inhibited by various
inhibitors\textsuperscript{9--11}.

At present, in-depth analysis of Hsp60 sequences \emph{in silico} is
quite rare, although it is a very interesting object for research. Since
Hsp60 is ubiquitous, it can be used in evolutionary analysis of
organisms belonging to different taxonomic
groups\textsuperscript{12--15}. Using bioinformatics, epitopes in Hsp60
can be predicted\textsuperscript{16,17}. In particular, an epitope-based
vaccine containing Hsp60 epitopes from \emph{Helicobacter pylori} was
tested in a model of a Mongolian gerbil infected with \emph{H. pylori}
\textsuperscript{16}. Oral immunization with this vaccine
reduced\emph{H. pylori} colonization due to the T helper, IgG, and IgA
responses and antibodies against various \emph{H. pylori} antigens. In
another study, B-cell epitopes were identified \emph{in silico} in Hsp60
overexpressed by tumor cells\textsuperscript{17}. In addition, it was
proposed to use the sequence of Hsp60 gene for the species-specific
identification of organisms belonging to the genus \emph{Acetobacter sp}
.\textsuperscript{18}, \emph{Helicobacter sp}
.\textsuperscript{19},\emph{Staphylococcus sp} .\textsuperscript{20},
\emph{Bifidobacterium sp} .\textsuperscript{21,22}, and
\emph{Bacteroidetes sp.} \textsuperscript{23} The interaction patterns
of Hsp60 and the A\selectlanguage{greek}β\selectlanguage{english}(1-42) peptide were predicted by molecular dynamics
simulation and protein-peptide docking\textsuperscript{24}. These
results are important because Hsp60 affects oligomers of the A\selectlanguage{greek}β \selectlanguage{english}peptide
reducing their cytotoxicity in patients with Alzheimer's
disease\textsuperscript{25}.

As you can see, Hsp60 has a fairly wide range of applications and
requires in-depth research. Therefore, the purpose of this study is a
comprehensive bioinformatic analysis of the amino acid and nucleotide
sequences of Hsp60 and the compilation of convenient databases on its
basis.

\textbf{Materials and methods}

\textbf{Databases preparation}

Database 1, containing 29360 amino acid sequences, was built with NCBI
BLAST at the following request:

\begin{itemize}
\tightlist
\item
  Database: non-redundant protein sequences (nr);
\item
  Algorithm: blastp (protein-protein BLAST);
\item
  Expected threshold: 10\textsuperscript{-20}.
\end{itemize}

Database 2, containing 2416 nucleotide sequences, was created using the
NCBI Gene Database (\emph{https://www.ncbi.nlm.nih.gov/gene/}).

The original databases contained a lot of junk items such as duplicates
of certain sequences and truncated sequences.

Truncated sequences were excluded according to the following criteria:
450 aa residues [?] number of amino acid residues in Hsp60 (amino acid
sequences) [?] 650 aa residues, 1350 bp [?] number of nucleotides in Hsp60
gene (nucleotide sequences) [?] 1950 bp. The criterion 99\% [?] PID [?] 100\%
was used to remove duplicate amino acid sequences, where the PID is the
percent identity of two compared sequences.

\textbf{Definition of taxonomy}

The search for taxonomic rank was carried out automatically using the
Selenium WebDriver library (Python
3.6;\emph{https://github.com/SeleniumHQ/selenium}). The databases
contain Hsp60 sequences of 19 phyla that were sorted by taxonomic rank
using the NCBI Taxonomy Database (https://www.ncbi.nlm.nih.gov/taxonomy)
(Table 1). Some of the sequences belonging to the super kingdom Viruses
have not been identified by phylum.

\textbf{Multiple sequence alignment}

Multiple alignment of Hsp60 amino acid sequences was performed using the
ClustalOmega software\textsuperscript{26} and the BioPython 1.68
library\textsuperscript{27}. Multiple alignment of Hsp60 nucleotide
sequences was performed using the MUSCLE program\textsuperscript{28} and
the MEGA-CC software\textsuperscript{29}. The nucleotide sequences were
aligned using the MUSCLE Codons option. Here, all codons in the
sequences have been converted to amino acids using the standard genetic
code. The obtained amino acid sequences were aligned with subsequent
reverse translation of amino acids into codons. Note that this option is
valid for encoding nucleotide sequences in multiples of three and
without reading frame displacement.

\textbf{Percent identity of sequences}

The percent of sequence identity (PID) (Supplementary, PID and SD) was
calculated according to equation (1):

\(\text{PID}=\ \frac{N_{\text{eq}}\bullet 100}{N_{1}+N_{2}-N_{\text{eq}}},\ \%\) (1)

where \emph{N\textsubscript{eq}} is the number of identical aligned
non-gapped characters; \emph{N\textsubscript{1}}
and\emph{N\textsubscript{2}} are the length of aligned
sequences\textsuperscript{30}. Note that equation 1 is applied to
full-sized sequences. In this study, the number of amino acid residues
(hereinafter referred to as the length of the sequences) ranged from 450
to 650 aa residues.

\textbf{Hierarchical clustering of sequences}

Clustering of values from a symmetrical matrix was performed using the
UPGMA algorithm and the SciPy library\textsuperscript{31,32}. The UPGMA
algorithm uses the Euclidean distance matrix as input. At each stage of
the process, the minimum value corresponding to the nearest clusters (A
and B) must be found in this matrix. Clusters A and B should be merged
into a new cluster C. The columns and rows corresponding to clusters A
and B should be replaced with a new column and row from cluster C. This
procedure should be repeated until all clusters are merged. During
clustering, the UPGMA algorithm builds a rooted-tree (dendrogram). The
length of the branches of this dendrogram corresponds to the Euclidean
distance between the two nearest clusters.

To separate clusters, you need to set the cophenetic distance. The
cophenetic distance is the height of the dendrogram where two branches,
including two objects, merge into one branch\textsuperscript{33}. In
this work, a cophenetic distance of 70\% of the final confluence height
on the dendrogram was used.

\textbf{Min-max normalization}

The min-max normalization strategy was used to compare two or more
samples by bringing the data in them to the same scale using the
following equation (2):

\(x_{\text{norm}}=\frac{x-x_{\min}}{x_{\max}-x_{\min}}\) (2),

where \emph{x\textsubscript{norm}} is the normalized value in the sample
in the range from 0 to 1; \emph{x} is the real value in the
sample;\emph{x\textsubscript{min}} and \emph{x\textsubscript{max}} are
the minimum and maximum values in the sample, respectively.

\textbf{Statistical analysis}

Statistical analysis was performed using the SciPy library (Python 3.6;
www.scipy.org\textsuperscript{31}). Data was presented as mean and
standard deviation and compared with Student's t-test. According to the
tests of statistical hypothesis used, the average values of the compared
samples differ non-significant (the difference between their mean values
is random) if the p-value of the statistical test is higher than the
significance level (alpha-error). An alpha error of 5\% was accepted for
all cases.

\textbf{Comparison of the amino acid composition of Hsp60 with the
proteomic composition}

Amino acid composition (\emph{X} ) as a percentage of 20 amino acid
residues in the sequence was determined for each of the Hsp60 sequences
in database 1. Then the average amino acid composition (\emph{X} \selectlanguage{english}) of
Hsp60s was determined for each of the 19 phyla.

To estimate the tendency to decrease or increase in the content of
certain amino acids in the Hsp60 sequences, it is necessary to calculate
the amino acid profile (\emph{X\textsubscript{n}}\selectlanguage{english} ). For this, the
average amino acid composition of Hsp60 (\emph{X} \selectlanguage{english}) for each of the 19
phyla was compared with the average amino acid composition of the
corresponding proteomes (\emph{X\textsubscript{p}}\selectlanguage{english} ) (Supplementary, AA
composition of proteomes) using the following conditions:

\begin{itemize}
\tightlist
\item
  If \emph{X\textsubscript{i}}\selectlanguage{english} \textgreater{}
  \emph{X\textsubscript{pi}}\selectlanguage{english}and p-value \textless{} alpha error, average
  percentage (\emph{X\textsubscript{i}}\selectlanguage{english} ) of \emph{i} -amino acid for
  Hsp60s is considered greater than proteomic value
  (\emph{X\textsubscript{pi}}\selectlanguage{english} ),\emph{X\textsubscript{ni}}\selectlanguage{english} = 1;
\item
  If \emph{X\textsubscript{i}}\selectlanguage{english} \textless{} \emph{X\textsubscript{pi}}\selectlanguage{english}and
  p-value \textless{} alpha error, average percentage content
  (\emph{X\textsubscript{i}}\selectlanguage{english} ) of \emph{i} -amino acid for Hsp60s is
  considered lower than proteomic value (\emph{X\textsubscript{pi}}\selectlanguage{english}
  ),\emph{X\textsubscript{ni}}\selectlanguage{english} = -1;
\item
  If the p-value of the statistical test is higher than the alpha error,
  the average percentage (\emph{X\textsubscript{i}}\selectlanguage{english} ) of \emph{i} -amino
  acid for Hsp60s is considered comparable to the proteomic value
  (\emph{X\textsubscript{pi}}\selectlanguage{english} ), \emph{X\textsubscript{ni}}\selectlanguage{english} = 0,
\end{itemize}

where \emph{X\textsubscript{ni}}\selectlanguage{english} is the normalized value of the average
percentage of \emph{i} -amino acid for Hsp60s (\emph{X\textsubscript{i}}\selectlanguage{english}
) relative to the average proteomic value (\emph{X\textsubscript{pi}}\selectlanguage{english} )
for the corresponding phylum.

The amino acid profile of phylum (\emph{X\textsubscript{n}}\selectlanguage{english} )
includes\emph{X\textsubscript{ni}}\selectlanguage{english} values for all amino acid residues.
Average proteomic amino acid compositions (\emph{X\textsubscript{p}}\selectlanguage{english} )
were calculated using 8747 reference proteomes obtained from the UniProt
Proteomes database (\emph{https://www.uniprot.org/proteomes/}).

Using the normalized values \emph{X\textsubscript{ni}}\selectlanguage{english} of 19 phyla, the
average normalized value \emph{{\guilsinglleft}X\textsubscript{ni}{\guilsinglright}} can be calculated.
The summary amino acid profile includes
\emph{{\guilsinglleft}X\textsubscript{ni}{\guilsinglright}}values for all phyla. In should be noted
that such an amino acid profile is only meaningful if the groups
included have similar amino acid profiles.

In this work, the amino acid profiles of the Hsp60 sequences for 19
phyla and the total amino acid profile were calculated.

\textbf{GC content}

The GC content reflects the percentage of guanine and cytosine bases in
the nucleotide sequence. The average GC content in Hsp60 sequences and
genomes for 17 phyla was calculated using database 2 and 20808 reference
genomes obtained from the NCBI Genome database
(\emph{https://www.ncbi.nlm.nih.gov/genome/}), respectively
(Supplementary, GC-content and ENC).

The GC content of the first, second and third codon positions
(GC\textsubscript{1}, GC\textsubscript{2}, and GC\textsubscript{3},
respectively) is required to evaluate their contribution to the GC
content and is calculated by equation (3):

\(\text{GC}_{n}=\ \frac{G_{n}+\ C_{n}}{N}\) (3),

where \emph{n} is the base position in the codon (\emph{n} = 1, 2,
3);\emph{G\textsubscript{n}} and \emph{C\textsubscript{n}} are the
number of guanine and cytosine bases in the n-position of the codons;
\emph{N}is the number of codons in the sequence.

\textbf{Neutrality analysis}

Neutrality plot analysis was used to assess codon usage bias caused by
mutational pressure and natural selection. Codon neutrality is expressed
through the relationship between GC\textsubscript{1,2} (average GC
content between GC\textsubscript{1} and GC\textsubscript{2}) and
GC\textsubscript{3}, represented by a regression line (neutrality
plot)\textsuperscript{34,35}. The slope \selectlanguage{greek}\emph{ε} \selectlanguage{english}of this regression
line, calculated using least squares regression analysis, is a
neutrality value that ranges from 0 when there is no effect of mutation
pressure (full selective constraints) to 1 when mutation bias is the
main force affecting the codon usage (complete neutrality of codon
usage). The equilibrium point \emph{E\textsubscript{p}} was also
defined. This point is at the intersection of the neutrality plot
(regression line) and the line of complete equilibrium between the
average GC content and the directional mutation pressure with neutrality
equal to one (\selectlanguage{greek}\emph{ε} \selectlanguage{english}= 1). The \emph{E\textsubscript{p}} value was
computed as\(\frac{\mathrm{A}_{\mathrm{0}}}{\left(\mathrm{1-\varepsilon}\right)}\), where \emph{A\textsubscript{0}} is the
value calculated from the regression of GC\textsubscript{1,2} and
GC\textsubscript{3} at GC\textsubscript{3} = 0\textsuperscript{34}. The
direction of the mutational pressure in the gene was defined according
to the position of this gene on the neutrality plot relative to the
equilibrium point.

\textbf{Relative synonymous codon usage (RSCU) analysis}

Relative synonymous codon usage (RSCU) is a measure for assessing the
uneven use of synonymous codons in nucleotide sequences or in the genome
and is calculated using equation (4):

\(RSCU=\ \frac{X_{i}}{\left(\frac{1}{n}\right)\sum_{i=1}^{n}X_{i}}\) (4),

where \emph{n} is the number of synonymous codons for the \emph{X}
-amino acid (1 [?] \emph{n} [?] 6); \emph{X\textsubscript{i}} is the number
of occurrences of the \emph{i} -codon in the nucleotide sequence. Codons
with RSCU \textgreater{} 1 are considered as frequent, and a higher RSCU
corresponds to a larger bias in codon usage. RSCU \textless{} 1
indicates the opposite situation. If the RSCU is one, no codon usage
bias is observed. The latter corresponds to the Met and Trp residues,
since these amino acids have no synonymous codons in accordance with the
standard genetic code (Supplementary, Genetic Code).

In this work, RSCU values were determined for each nucleotide sequences
from database 2. Average RSCU values were determined for 17 phyla,
excluding Viruses and Platyhelminthes (Supplementary, RSCU).

\textbf{Effective number of codons}

Effective number of codons (ENC) is a measure for assessing codon usage
bias. The ENC value ranges from 20 (maximum bias, i.e. only one codon is
used to encode an amino acid) to 61 (minimum bias, i.e. codons are used
randomly)\textsuperscript{36,37}. In contrast to the RSCU value, the ENC
value characterizes the overall codon usage bias for a gene/genome.

The calculation of codon homozygosity (\emph{F\textsubscript{k}} ) for
all amino acids having synonymous codons, which is required to calculate
the ENC value, was carried out according to the following equations (5):

\(S=\ \sum_{i=1}^{k}{\left(\frac{n_{i}}{n}\right)^{2};\ \ \ \ \ F_{k}=\frac{nS-1}{n-1}}\) (5),

where \emph{n\textsubscript{i}} is the occurrence of the \emph{k} -codon
for the \emph{i} -amino acid; \emph{n} is the total number of codons for
the \emph{i} -amino acid; \emph{k} is the number of codons for
the\emph{i} -amino acid (\emph{k} = 2, 3, 4, 6). In this
work,\emph{n\textsubscript{i}} and \emph{n} were represented by the
average values for Hsp60 sequences of 17 phyla excluding Viruses and
Platyhelminthes (Supplementary, GC-content and ENC).

The ENC values were calculated for 17 phyla according to equations (6):

\(ENC=2+\frac{9}{F_{2}}+\frac{1}{F_{3}}+\frac{5}{F_{4}}+\frac{3}{F_{6}};\ \ \ \ \ \text{ENC}_{\exp}=2.5+s+\frac{29.5}{s^{2}+{(1-s)}^{2}}\) (6)

where \emph{2} is a sum of \emph{F} for Met and Trp, since these amino
acid residues have no synonymous codons;
\emph{F\textsubscript{2/3/4/6}}is an average \emph{F\textsubscript{k}}
for those amino acid residues that have \emph{k} synonymous codons. The
\emph{ENC\textsubscript{exp}}value is the expected ENC value for various
GC contents at the third synonymous codon position (GC\textsubscript{3}
content) in the Hsp60 gene for 17 phyla. Here, \emph{s} is the given
value of GC\textsubscript{3} content (Supplementary, GC-content and
ENC). The data obtained were used to construct the Nc-plot, where the
ENC values are presented as a scatter plot, and the
\emph{ENC\textsubscript{exp}}values are presented as a solid curve. Gene
codons are unbiased if the corresponding point is on the expected
curve\textsuperscript{38}.

\textbf{Results and discussion}

\textbf{Databases}

According to the main criteria (see Databases preparation) databases
were compiled containing amino acid (database 1) and nucleotide
(database 2) sequences of Hsp60. The size of database 1 was 19220 amino
acid sequences and the size of database 2 was 1925 nucleotide sequences.
The composition of these databases is described in details in Table 1.

\textbf{Table 1.} General composition of databases\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Run\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Kingdom\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Phylum\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Number of AA sequences in the group (database 1)\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Average PID\selectlanguage{ngerman}±SD, \%\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Number of subclusters\textsuperscript{a}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Number of nucleotide sequences in the group (database 2)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Viruses\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Viruses\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
79\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
28\selectlanguage{ngerman}±4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Archaea\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Euryarchaeota\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
151\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
42\selectlanguage{ngerman}±15\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
63\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chlamydiae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
122\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
31\selectlanguage{ngerman}±10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteroidetes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1358\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
58\selectlanguage{ngerman}±4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
22\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Proteobacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7973\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
51\selectlanguage{ngerman}±9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
336\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Cyanobacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
682\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
53\selectlanguage{ngerman}±6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Actinobacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4309\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
52\selectlanguage{ngerman}±7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
73\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Firmicutes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3132\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
56\selectlanguage{ngerman}±8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
132\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Protozoa\textsuperscript{b}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Apicomplexa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
47\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
42\selectlanguage{ngerman}±14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
16\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Protozoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Euglenozoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
50\selectlanguage{ngerman}±14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Plantae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Streptophyta\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
717\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
44\selectlanguage{ngerman}±17\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
431\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Plantae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chlorophyta\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
26\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
41\selectlanguage{ngerman}±8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Fungi\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Ascomycota\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
228\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
64\selectlanguage{ngerman}±7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
136\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Fungi\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Basidiomycota\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
79\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
63\selectlanguage{ngerman}±3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
31\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Nematoda\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
50\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
72\selectlanguage{ngerman}±19\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Arthropoda\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
162\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
50\selectlanguage{ngerman}±10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
255\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
17\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Platyhelminthes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
66\selectlanguage{ngerman}±5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chordata\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
61\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
84\selectlanguage{ngerman}±6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
384\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Mollusca\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
67\selectlanguage{ngerman}±4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
19220\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
60\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1925\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{a} Clustering was carried out in each of 19 phyla using
the UPGMA algorithm. \textsuperscript{b} The designation ``Protozoa''
was used to identify Apicomplexa and Euglenozoa, since kingdom of these
phyla is not determined in the NCBI Taxonomy.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{a} Clustering was carried out in each of 19 phyla using
the UPGMA algorithm. \textsuperscript{b} The designation ``Protozoa''
was used to identify Apicomplexa and Euglenozoa, since kingdom of these
phyla is not determined in the NCBI Taxonomy.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{a} Clustering was carried out in each of 19 phyla using
the UPGMA algorithm. \textsuperscript{b} The designation ``Protozoa''
was used to identify Apicomplexa and Euglenozoa, since kingdom of these
phyla is not determined in the NCBI Taxonomy.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{a} Clustering was carried out in each of 19 phyla using
the UPGMA algorithm. \textsuperscript{b} The designation ``Protozoa''
was used to identify Apicomplexa and Euglenozoa, since kingdom of these
phyla is not determined in the NCBI Taxonomy.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{a} Clustering was carried out in each of 19 phyla using
the UPGMA algorithm. \textsuperscript{b} The designation ``Protozoa''
was used to identify Apicomplexa and Euglenozoa, since kingdom of these
phyla is not determined in the NCBI Taxonomy.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{a} Clustering was carried out in each of 19 phyla using
the UPGMA algorithm. \textsuperscript{b} The designation ``Protozoa''
was used to identify Apicomplexa and Euglenozoa, since kingdom of these
phyla is not determined in the NCBI Taxonomy.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{a} Clustering was carried out in each of 19 phyla using
the UPGMA algorithm. \textsuperscript{b} The designation ``Protozoa''
was used to identify Apicomplexa and Euglenozoa, since kingdom of these
phyla is not determined in the NCBI Taxonomy.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Comparative analyses of amino acid sequences of Hsp60 from 19
phyla}

\textbf{PID estimation}

Average PID values were calculated for 19220 Hsp60 sequences in 19 phyla
(Table 1), where they ranged from 28\selectlanguage{ngerman}±4\% (Viruses) to 84±6\% (Chordata).
The maximum average PID value was determined for the Hsp60 sequences
belonging to Chordata, which was expected in accordance with our
previous work\textsuperscript{14}. The minimum average PID value
corresponds to Viruses, which can be explained by their high mutation
rate. For groups with a higher taxonomic rank, the average PID values
varied within a narrow range from 51±9\% to 58±4\% for Bacteria
(Proteobacteria, Cyanobacteria, Actinobacteria, Firmicutes, and
Bacteroidetes, with the exception of Chlamydiae), from 63±3\% to 64±7\%
for Fungi (Basidiomycota and Ascomycota), and from 41±18\% to 44±17\%
for Plantae (Chlorophyta and Streptophyta). Apicomplexa and Euglenozoa
can be combined into Protozoa with the average PID values ranging from
42±14\% to 50±14\%. For Hsp60 sequences belonging to Metazoa
(Arthropoda, Mollusca, Platyhelminthes, Nematoda, and Chordata), the
range of average PID values was wider: from 50±10\% for Arthropoda to
84±6\% for Chordata.

Clustering was performed on 19 phyla, and the average PID value was
calculated for each of the sub-clusters (Supplementary, PID, and SD).
The number of sub-clusters ranged from two to seven in 19 phyla (Table
1). The calculation of the average PID values and their clustering were
carried out between 60 sub-clusters (Figure 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Symmetric matrix of the average PID values. The
matrix contains 60 sub-clusters of Hsp60 sequences from 19 phyla. The X-
and Y-axis items ``Sub-cluster'' are represented in the following format
``Phylum \#sub-cluster (number of sequences in a sub-cluster)''.
Sub-clusters of Viruses have no phylum labels. The Y-axis ``Kingdom''
represents sub-clusters united by a higher taxonomic rank (Kingdom). The
black frames and the X-axis ``Cluster'' show four clusters and their
numbers (Roman numerals), which were obtained by clustering 60
sub-clusters. Clustering was performed using the UPGMA algorithm.

Apparently, the symmetric matrix contains four clusters. Cluster I
contains 258 Hsp60 sequences from Metazoa (multicellular animals). It
should be noted that, as a rule, in this cluster the average PID values
are more than 60\% and 50\% within and between sub-clusters,
respectively (Supplementary, PID, SD). The number of identical amino
acid residues in the sequences reflects the degree of conservatism.
Thus, the Hsp60 sequences belonging to cluster I can be considered as
intermediate and highly conserved, as noted in our previous
work\textsuperscript{14}. Cluster II includes 608 Hsp60 sequences of
Fungi, Plantae, Protozoa, and Metazoa. It should be noted that fungal
Hsp60s were clustered into a small group with average PID values greater
than 60\% and 50\% within and between sub-clusters, respectively, as
observed for cluster I. Thus, the Hsp60 amino acid sequences from Fungi
can also be classified as intermediate and highly conserved. Others
sub-clusters in cluster II demonstrate intermediate and low sequence
conservatism, with average PID values ranging from 40\selectlanguage{ngerman}±30\% to 62±13\%
within sub-clusters and from 24±4\% to 47±19\% between them.

The largest cluster III contains 18244 sequences (22 sub-clusters),
mainly including Hsp60 of Bacteria, Plantae, and Archaea. It should be
noted that within the 11 sub-clusters in cluster III, the average PID
values vary from 53±10\% (Firmicutes \#2) to 73±8\% (Cyanobacteria \#1),
indicating intermediate and highly conserved Hsp60 sequences. However,
between the sub-clusters of cluster III, these values are less than
50\%. Thus, it can be assumed that, in the whole, the level of
conservatism in cluster III is low.

Finally, there is the smallest cluster IV containing 110 Hsp60 sequences
from Viruses, Bacteria, Protozoa, and Metazoa. In this cluster,
sub-clusters of Viruses \#1, Apicomplexa \#3, Chlamydia \#1, and
Apicomplexa \#2 show average PID values of more than 50\%. On the other
hand, the PID values between the sub-clusters of cluster IV are quite
low and range from 10±1\% (Chlamydiae \#1/Apicomplexa \#2) to 33±2\%
(Apicomplexa \#2/Apicomplexa \#3). Moreover, the average PID values
between cluster IV and other clusters are also low (Figure 1). This
extremely low level of conservatism of Hsp60 sequences in cluster IV may
indicate how these Hsp60s have evolved to distance themselves from
others Hsp60.

In some studies\textsuperscript{2,4,17,25} Hsp60 is called a highly
conserved protein. But, according to the obtained data, the percent of
identical amino acid residues varies widely. Thus, Hsp60 is not a highly
conserved protein.

It should be noted that metazoan Hsp60 sequences belonging to
sub-clusters Arthropoda \#2 and \#3, and Nematoda \#1 were not included
in cluster I. To explain this phenomenon using a symmetric PID matrix
(Supplementary, PID), the average PID values between Hsp60 sequences of
these sub-clusters and others Hsp60 sequences belonging to each of 19
phyla were calculated (Figure 2; Supplementary, Metazoan artifacts).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Heat map showing normalized average PID values
between Hsp60 sequences belonging to sub-clusters Arthropoda \#2,
Arthropoda \#3, and Nematoda \#1 and Hsp60 sequences belonging to each
of 19 phyla. The average PID values were normalized using the min-max
normalization method. The 19 phyla were sorted by NCBI Taxonomy.

According to Figure 2, the highest average PIDs were observed between
Arthropoda sub-clusters \#2 and \#3 and phyla Mollusca and Arthropoda,
respectively. Since these phyla belong to Metazoa, the presence of these
sub-clusters in clusters II and IV can be explained as a feature of
dendrogram construction and the selected percent of cophenetic distance
(see Hierarchical clustering of sequences).

On the other hand, the average PID value between the Nematoda \#1
sub-cluster and Proteobacteria phylum was 37.8\selectlanguage{ngerman}±0.3\% (Supplementary,
Metazoan artifacts), which was higher than for all metazoan phyla
including Nematoda (28.7±0.9\%). This feature may be the result of a
database error.

In turn, this may be explained by horizontal gene
transfer\textsuperscript{39}. Accordingly, possible cases of horizontal
Hsp60 gene transfer were identified using the obtained data
(Supplementary, Horizontal Hsp60 gene transfer). It was found that
horizontal Hsp60 gene transfer is most common in Bacteria, which is
obvious. In turn, the Hsp60 sequences from the Nematoda \#1 sub-cluster
have the maximum PID in the following pairs:

\emph{Onchocerca volvulus} (CAA70570.1, Nematoda)/\emph{Wolbachia
sp.}(WP\_014869024.1, Proteobacteria), PID 78.3\%;

\emph{Trichuris trichiura} (CDW56974.1, Nematoda)/\emph{Enterobacter
sp.} (WP\_015572446.1, Proteobacteria), PID 88.6\%.

Hence, the transmission paths can be tracked. Endosymbiotic
bacteria\emph{Wolbachia sp.} are present in filarial
nematodes\textsuperscript{40}, to which \emph{Onchocerca
volvulus}belongs. At the same time, \emph{Trichuris trichiura}
roundworms parasitize in human intestine, where \emph{Enterobacter sp.}
bacteria are also present. These data are not conclusive evidence of
horizontal Hsp60 gene transfer between these organisms and require
in-depth review.

\textbf{Amino acid composition of Hsp60}

The amino acid composition of 19220 Hsp60 sequences in database 1 and
the average amino acid compositions of Hsp60s for 19 phyla were
calculated (Supplementary, AA composition of Hsp60). Despite the low
average PID values between sub-clusters (Figure 1), the amino acid
composition was quite similar (Figure 3a).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} The average amino acid composition of the Hsp60
sequences from 19 phyla: a - Heat map displaying normalized average PID
values between 19 phyla of Hsp60 sequences; b - The average amino acid
composition of the Hsp60 sequences for each of the 19 phyla compared to
the corresponding proteomic values. In Figure 3a the average values were
normalized using the min-max normalization method. The line ``Summary''
presents the average normalized amino acid composition of Hsp60 for 19
phyla. In Figure 3b the amino acid profiles were represented as the
average amino acid composition of Hsp60 for each of 19 phyla compared to
the average amino acid composition of the respective proteomes. The
structure of color scale is as follows: Higher/Lower - the amino acid
content in Hsp60 is higher/lower than in proteomes, respectively;
Comparable - the amino acid content in Hsp60 is comparable to the
average proteomic value. The ``Summary'' line shows the average amino
acid profile of Hsp60 for 19 phyla. The groups were sorted using the
NCBI Taxonomy. Amino acids were sorted using an average amino acid
composition of 19220 Hsp60 sequences.

As can be seen, aliphatic (Ala, Val, Gly, Leu, and Ile), charged (Glu,
Lys, Asp), and polar (Thr) amino acid residues are overrepresented in
the Hsp60 sequences of all 19 phyla (Figure 3a). The average content of
these amino acid residues for 19220 Hsp60 sequences ranges from
6.2\selectlanguage{ngerman}±1.1\% for Thr to 12.7±1.9 for Ala (Supplementary, AA composition of
Hsp60). In turn, aromatic amino acid residues (Phe, Tyr, His, and Trp)
and Cys are underrepresented and their content ranges from 0.2±0.3\%
(Cys) to 1.6±0.4\% (Phe) (Figure 3a; Supplementary, AA composition of
Hsp60).

To assess the amino acid composition of Hsp60s relative to proteomic
values (Supplementary, AA Composition of proteomes), amino acid profiles
of Hsp60 sequences were determined for each of 19 phyla (Figure 3b).

The average content of aliphatic (Ala, Val, Gly, and Ile), charged (Glu,
Lys, and Asp), and polar (Thr) amino acid residues in Hsp60 sequences is
not only high, but also higher than the average proteomic values for
almost all phyla (Figure 3b). The high content of aliphatic amino acid
residues may indicate the structural stability of these
proteins\textsuperscript{15}. The content of Pro, aromatic (Phe, Tyr,
His, and Trp) and polar (Ser and Cys) amino acid residues was low and
lower compared to the average proteomic values. In general, according to
the summary amino acid profile, no matter how evolutionarily distant the
19 phyla are from each other, the amino acid composition of their Hsp60
sequences remained the same, as its correlation with the amino acid
composition of the corresponding proteome.

Despite the low PID values both within and between phyla, functionality
and domain structure\textsuperscript{41--45} of Hsp60 persisted over
time. It can be assumed that these features largely depended on the
amino acid composition, i.e. on the percentage content of amino acids in
Hsp60.

\textbf{Comparative analyses of Hsp60 genes from 17 phyla}

\textbf{GC content and mutation pressure for codon usage}

In the present study, the average GC content and
GC\textsubscript{1/2/3}for the Hsp60 sequences and the average genomic
GC content were calculated for 17 phyla (Figure 4a; Supplementary,
GC-content and ENC).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} The average nucleotide composition of the Hsp60 genes
from 17 phyla: a -- The average total GC contents at each positions of
codon of Hsp60 sequences and corresponding genomes; b - The average
content of GC\textsubscript{1}, GC\textsubscript{2}, and
GC\textsubscript{3} in Hsp60 genes. Phyla were sorted by average total
GC content of Hsp60 sequences. Student's t-test was used to compare the
average GC content of the Hsp60 sequences and the average GC content of
the corresponding genomes. The difference between two independent
samples of GC values is considered statistically significant if the
p-value is less than 0.05. Statistically indistinguishable average GC
values are marked with ``ns'' (non-significant).

The average GC content in the Hsp60 genes ranges from 0.41\selectlanguage{ngerman}±0.13
(Apicomplexa) to 0.67±0.04 (Actinobacteria) (Figure 4a). As can be seen,
the average GC content of almost all Hsp60 genes is comparable to or
exceeds the average genomic background. In turn, the opposite is
observed for Euryarchaeota. The upward trend in the GC content in the
Hsp60 genes may be associated with recombination (GC-biased gene
conversion)\textsuperscript{46,47}, repair\textsuperscript{48}, and the
environmental changes\textsuperscript{49,50}, in which there is an
increase in the frequency of AT\selectlanguage{english}-GC substitutions. Thus, it can be
assumed that the Hsp60 gene is tightly controlled by DNA repair systems
that protect the genetic material from mutations.

To determine the contribution of each of three codon positions to the
total GC content of Hsp60 genes from 17 phyla, the GC\textsubscript{1},
GC\textsubscript{2}, and GC\textsubscript{3} contents were calculated.
The average GC\textsubscript{1} values vary from 0.49+-0.06 (Apicomplexa)
to 0.69+-0.02 (Actinobacteria), and their contribution to the total GC
content of Hsp60 genes is moderate (Figure 4b). At the same time,
GC\textsubscript{2} in the range from 0.36+-0.03 (Apicomplexa) to
0.44+-0.02 (Basidiomycota) were the least variable and practically did
not affect the GC composition of the Hsp60 genes. These results are
obvious since the second codon position is the most conserved.
GC\textsubscript{3} values vary from 0.25+-0.15 (Firmicutes) to 0.9+-0.11
(Actinobacteria), which indicates a mutation bias\textsuperscript{51}.
It should be noted that starting from Euryarchaeota, the average
GC\textsubscript{3} values of the Hsp60 genes increase sharply (Figure
4b), and the average GC content becomes more than 0.5 (Figure 4a).

The substitution of nucleotides at the third position of the codon,
caused by point mutations or repair processes, does not change the amino
acid, but only indicates the mutational pressure for codon usage.
According to the theory\textsuperscript{35}, mutational pressure tends
to push the GC content in a gene/genome towards equilibrium (neutrality
of codon usage), reducing the heterogeneity caused by natural
selection\textsuperscript{34}. Equilibrium of the nucleotide composition
of the gene/genome, in which selective constraints (factors that reduced
the evolutionary divergence of the functional sequence) do not affect
the GC content, is achieved when the frequencies of the AT\selectlanguage{english}-GC and GC\selectlanguage{english}-AT
mutations are equal\textsuperscript{34}. These mutations can be fixed or
removed from the population by natural selection and random genetic
drift\textsuperscript{35}. The frequencies of the AT\selectlanguage{english}-GC and GC\selectlanguage{english}-AT
mutations at the third position of the codons also reflect the direction
of the mutational pressure. In general, the GC\textsubscript{3} value is
less than 0.5 when the gene is under the influence of AT pressure,
and\emph{vice versa} \textsuperscript{52}. Thus, we can initially
identify two groups of Hsp60 genes that differ in the direction of
mutational pressure (Figure 4b). The AT-group includes Apicomplexa,
Chlamydiae, Firmicutes, Streptophyta, Nematoda, Bacteroidetes, Mollusca,
Cyanobacteria, and Chordata, which have an average total GC content of
less than 0.5 in the Hsp60 genes. In turn, the phyla Euryarchaeota,
Arthropoda, Ascomycota, Proteobacteria, Euglenozoa, Basidiomycota,
Chlorophyta, and Actinobacteria form a GC-group with an average total GC
content of more than 0.5. However, the threshold of 0.5 is nominal due
to the imbalance between the rates of mutation and repair
processes\textsuperscript{53}. Therefore, a neutrality analysis was
carried out to clarify the direction of the mutational pressure and to
reveal the degree of its influence on the codon usage with the
determination of the equilibrium point (Figure 5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Neutrality plots (GC\textsubscript{1,2} vs.
GC\textsubscript{3}) for Hsp60 genes from 17 phyla. The
GC\textsubscript{1,2} values represent the average GC content at the
first and second positions of codon (GC\textsubscript{1} and
GC\textsubscript{2}), while GC\textsubscript{3} values represent the GC
content at the third synonymous codon position. The solid line
represents the linear regression of GC\textsubscript{1,2} versus
GC\textsubscript{3}, the correlation of which is described by the
regression coefficient R and its p-value. The correlation coefficient R
reflects the strength of the impact of GC\textsubscript{3} on
GC\textsubscript{1,2}. The p-value characterizes the significance of R.
Changes in GC\textsubscript{3} values actually affect the
GC\textsubscript{1,2} values when the p-value of R is less than 0.05. In
turn, changes in GC\textsubscript{3} are considered random, and the R
coefficient is not irrelevant when the p-value is greater than 0.05,
i.e. GC\textsubscript{3} and GC\textsubscript{1,2} values are not
correlated. The slope \selectlanguage{greek}\emph{ε} \selectlanguage{english}of the regression line indicates the
neutrality of the codon usage. Neutrality values were determined by
equation {[}\selectlanguage{greek}\emph{ε} \selectlanguage{ngerman}× 100, \%{]}. Slope values ranging from 0 to 1 were
calculated using the least-squares regression analysis. The dashed line
is a complete neutrality plot, which reflects the complete equilibrium
of the nucleotide composition of the gene/genome with directional
mutation pressure. The equilibrium point \emph{E\textsubscript{p}} was
defined as the intersection point of the neutrality plot (regression
line) and the complete neutrality plot. The
\emph{E\textsubscript{p}}value reflects the GC\textsubscript{3} content
of the gene/genome when the mutation frequencies (AT\selectlanguage{english}-GC and GC\selectlanguage{english}-AT) are
equal. The direction of the mutational pressure, indicating an imbalance
in the frequencies of the AT\selectlanguage{english}-GC and GC\selectlanguage{english}-AT mutations, was determined in
accordance with the following conditions: the average GC content value
less than the\emph{E\textsubscript{p}} value reflects the AT mutational
pressure; the average GC content value greater than the
\emph{E\textsubscript{p}} value reflects the GC mutational pressure.
Phyla were sorted by average total GC content of Hsp60 genes.

The results of the neutrality analysis reflect statistically significant
correlations between the GC\textsubscript{1,2} and
GC\textsubscript{3}values of the Hsp60 genes from 15 phyla (Figure 5).
Taking into account the results of neutrality analysis and difference
between the average GC\textsubscript{3} values and the corresponding
values at equilibrium points, 17 phyla were divided into three groups
according to the direction of mutational pressure (Table 2).

\textbf{Table 2.} The results of neutrality plot analysis\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Run\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Kingdom\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Phylum\textsuperscript{b}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Average GC\textsubscript{3} content\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Neutrality, \selectlanguage{greek}\emph{ε}\selectlanguage{ngerman}×100\%\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Equilibrium point \(\mathrm{E}_{\mathrm{p}}^{\mathrm{c}}\)\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Direction of mutational pressure\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Protozoa\textsuperscript{a}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Apicomplexa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.35\selectlanguage{ngerman}±0.21\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.45\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chlamydiae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.3\selectlanguage{ngerman}±0.04\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.3\textsuperscript{*}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.46\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Firmicutes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.29\selectlanguage{ngerman}±0.13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.51\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Plantae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Streptophyta\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.39\selectlanguage{ngerman}±0.04\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
13.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Nematoda\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.38\selectlanguage{ngerman}±0.14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteroidetes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.38\selectlanguage{ngerman}±0.19\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Mollusca\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.4\selectlanguage{ngerman}±0.13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.47\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Cyanobacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.38\selectlanguage{ngerman}±0.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.7\textsuperscript{*}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.51\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chordata\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.45\selectlanguage{ngerman}±0.17\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT/GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Metazoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Arthropoda\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.55\selectlanguage{ngerman}±0.15\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT/GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Proteobacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.54\selectlanguage{ngerman}±0.21\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.52\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AT/GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Archaea\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Euryarchaeota\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.64\selectlanguage{ngerman}±0.13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
32.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.44\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Fungi\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Ascomycota\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.62\selectlanguage{ngerman}±0.23\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
11.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Protozoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Euglenozoa\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.65\selectlanguage{ngerman}±0.11\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.51\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Fungi\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Basidiomycota\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.67\selectlanguage{ngerman}±0.14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.50\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Plantae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chlorophyta\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.7\selectlanguage{ngerman}±0.16\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
17.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.46\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
17\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Actinobacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.87\selectlanguage{ngerman}±0.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.54\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GC\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Note: The neutrality values with statistically non-significant
correlation coefficient R (p-value \textgreater{} 0.05) are marked with
symbol (*). \textsuperscript{a} The designation ``Protozoa'' was used to
identify Apicomplexa and Euglenozoa, since kingdom of these phyla is not
determined in the NCBI Taxonomy. \textsuperscript{b} Phyla were divided
by the direction of the mutational pressure and sorted by average GC
content. \textsuperscript{c} The value of equilibrium point was
calculated as \(\frac{\mathrm{A}_{\mathrm{0}}}{\left(\mathrm{1-\varepsilon}\right)}\), where A\textsubscript{0} is a value
computed from regression of GC\textsubscript{1,2} and
GC\textsubscript{3} at GC\textsubscript{3} = 0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Note: The neutrality values with statistically non-significant
correlation coefficient R (p-value \textgreater{} 0.05) are marked with
symbol (*). \textsuperscript{a} The designation ``Protozoa'' was used to
identify Apicomplexa and Euglenozoa, since kingdom of these phyla is not
determined in the NCBI Taxonomy. \textsuperscript{b} Phyla were divided
by the direction of the mutational pressure and sorted by average GC
content. \textsuperscript{c} The value of equilibrium point was
calculated as \(\frac{\mathrm{A}_{\mathrm{0}}}{\left(\mathrm{1-\varepsilon}\right)}\), where A\textsubscript{0} is a value
computed from regression of GC\textsubscript{1,2} and
GC\textsubscript{3} at GC\textsubscript{3} = 0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Note: The neutrality values with statistically non-significant
correlation coefficient R (p-value \textgreater{} 0.05) are marked with
symbol (*). \textsuperscript{a} The designation ``Protozoa'' was used to
identify Apicomplexa and Euglenozoa, since kingdom of these phyla is not
determined in the NCBI Taxonomy. \textsuperscript{b} Phyla were divided
by the direction of the mutational pressure and sorted by average GC
content. \textsuperscript{c} The value of equilibrium point was
calculated as \(\frac{\mathrm{A}_{\mathrm{0}}}{\left(\mathrm{1-\varepsilon}\right)}\), where A\textsubscript{0} is a value
computed from regression of GC\textsubscript{1,2} and
GC\textsubscript{3} at GC\textsubscript{3} = 0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Note: The neutrality values with statistically non-significant
correlation coefficient R (p-value \textgreater{} 0.05) are marked with
symbol (*). \textsuperscript{a} The designation ``Protozoa'' was used to
identify Apicomplexa and Euglenozoa, since kingdom of these phyla is not
determined in the NCBI Taxonomy. \textsuperscript{b} Phyla were divided
by the direction of the mutational pressure and sorted by average GC
content. \textsuperscript{c} The value of equilibrium point was
calculated as \(\frac{\mathrm{A}_{\mathrm{0}}}{\left(\mathrm{1-\varepsilon}\right)}\), where A\textsubscript{0} is a value
computed from regression of GC\textsubscript{1,2} and
GC\textsubscript{3} at GC\textsubscript{3} = 0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Note: The neutrality values with statistically non-significant
correlation coefficient R (p-value \textgreater{} 0.05) are marked with
symbol (*). \textsuperscript{a} The designation ``Protozoa'' was used to
identify Apicomplexa and Euglenozoa, since kingdom of these phyla is not
determined in the NCBI Taxonomy. \textsuperscript{b} Phyla were divided
by the direction of the mutational pressure and sorted by average GC
content. \textsuperscript{c} The value of equilibrium point was
calculated as \(\frac{\mathrm{A}_{\mathrm{0}}}{\left(\mathrm{1-\varepsilon}\right)}\), where A\textsubscript{0} is a value
computed from regression of GC\textsubscript{1,2} and
GC\textsubscript{3} at GC\textsubscript{3} = 0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Note: The neutrality values with statistically non-significant
correlation coefficient R (p-value \textgreater{} 0.05) are marked with
symbol (*). \textsuperscript{a} The designation ``Protozoa'' was used to
identify Apicomplexa and Euglenozoa, since kingdom of these phyla is not
determined in the NCBI Taxonomy. \textsuperscript{b} Phyla were divided
by the direction of the mutational pressure and sorted by average GC
content. \textsuperscript{c} The value of equilibrium point was
calculated as \(\frac{\mathrm{A}_{\mathrm{0}}}{\left(\mathrm{1-\varepsilon}\right)}\), where A\textsubscript{0} is a value
computed from regression of GC\textsubscript{1,2} and
GC\textsubscript{3} at GC\textsubscript{3} = 0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Note: The neutrality values with statistically non-significant
correlation coefficient R (p-value \textgreater{} 0.05) are marked with
symbol (*). \textsuperscript{a} The designation ``Protozoa'' was used to
identify Apicomplexa and Euglenozoa, since kingdom of these phyla is not
determined in the NCBI Taxonomy. \textsuperscript{b} Phyla were divided
by the direction of the mutational pressure and sorted by average GC
content. \textsuperscript{c} The value of equilibrium point was
calculated as \(\frac{\mathrm{A}_{\mathrm{0}}}{\left(\mathrm{1-\varepsilon}\right)}\), where A\textsubscript{0} is a value
computed from regression of GC\textsubscript{1,2} and
GC\textsubscript{3} at GC\textsubscript{3} = 0\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

According to the data obtained, the main direction of mutational
pressure was determined for Hsp60 genes of 14 phyla, with the exception
of Chordata, Arthropoda, and Proteobacteria (Table 2). However, the
peculiarities of the neutrality plot of Chordata should be noted (Figure
6).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6.} Neutrality plot for Hsp60 genes of Chordata

As can be seen, the Hsp60 genes can be divided into two groups. The
AT-biased group includes the Hsp60 genes from Mammalia, Aves, Reptilia,
and Amphibia. In turn, the second group with GC-bias includes all Hsp60
genes from Fish and five Hsp60 genes belonging to Hyperoartia
(\emph{Petromyzon marinus} , GC\textsubscript{3} 0.92), Mammalia
(\emph{Ornithorhynchus anatinus} , GC\textsubscript{3}
0.88;\emph{Lipotes vexillifer} , GC\textsubscript{3} 0.76), and
Leptocardii (\emph{Branchiostoma floridae} , GC\textsubscript{3} 0.62;
\emph{Gekko japonicus} , GC\textsubscript{3} 0.52). Interestingly, four
of these organisms, with the exception of \emph{Gekko japonicus} , are
aquatic. On the other hand, there is the Hsp60 gene from
\emph{Boleophthalmus pectinirostris} , belonging to Fish, with
GC\textsubscript{3} of 0.496, which is closest to the equilibrium
GC\textsubscript{3} of Chordata. This mudskipper is an amphibious fish
capable of feeding on land. It has been suggested that codon usage bias
due to substitutions of synonymous codons may be associated with the
lifestyle of organisms\textsuperscript{54--56}. Based on this, we can
assume that the habitat or specific diet can influence the synonymous
codon substitution in the Hsp60 genes of the mentioned organisms.

Neutrality of codon usage for all phyla, with the exception of
Euryarchaeota, was less than 20\%, indicating that natural selection
dominates mutational pressure at the third position of codons in these
Hsp60 genes (Table 2). At the same time, the neutrality of 32.4\% for
the Hsp60 genes from Euryarchaeota is the highest among all 17 phyla and
indicates a rather high degree of mutational pressure. The high
correlation coefficient R of 0.865 reflects the important role of
mutational pressure as a factor affecting codon
usage\textsuperscript{57}.

Thus, the high resistance to spontaneous mutations provided by DNA
repair systems, low mutational pressure, and constant amino acid
composition (see Amino acid composition of Hsp60) suggest that the Hsp60
gene is a trait inherent in all organisms\textsuperscript{58}.

\textbf{Codon usage biases}

The degree of codon usage bias, or, in other words, the size of the
``codon dictionary'' needs to be assessed. The effective number of
codons (ENC) is one of the most widely used metrics for this purpose. In
this study, ENC values were calculated using the
GC\textsubscript{3}content of the Hsp60 genes for each of the 17 phyla
(Figure 7).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 7.} Nc-plots of codon usage bias in Hsp60 genes from the
17 phyla. Gray scatter plots represent ENC values versus
GC\textsubscript{3} content for Hsp60 genes from 17 phyla. The black
bell-shaped curves represent the expected effective number of codons
(\emph{ENC\textsubscript{exp}} ), i.e. predicted ENC values if codon
usage bias is influenced by GC\textsubscript{3} content (GC content at
the third synonymous position of codons) in the Hsp60 gene only. Phyla
were sorted by the average total GC content of Hsp60 genes.

Almost all Hsp60 genes have lower ENC values than expected, suggesting
that the pressure of mutations affects codon usage\textsuperscript{59}.
As with the neutrality plot analysis (see above), attention should be
paid to the Nc-plot for Chordata, where the ENC values are divided.
Accordingly, the ENC values of the Hsp60 genes from Chordata were
grouped using taxonomy (class) (Figure 8).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 8.} Clustering of ENC values for Hsp60 genes from
Chordata. Cluster \#1 includes the ENC values of Hsp60 genes of
Mammalia, Aves, Reptilia, and Amphibia. Cluster \#2 includes ENC values
of Hsp60 genes of Fish. Clustering was performed using the value of the
GC\textsubscript{3} content corresponding to the equilibrium
point\emph{E\textsubscript{p}} , which was determined earlier (see
GC-content and mutation pressure for codon usage).

As can be seen, the ENC values of the Hsp60 genes from Chordata form two
groups (Figure 8). Hsp60 genes from cluster \#1 (GC\textsubscript{3} is
less than \emph{E\textsubscript{p}} ) belong to Mammalia, Aves,
Reptilia, and Amphibia. In turn, cluster \#2 (GC\textsubscript{3} is
larger than\emph{E\textsubscript{p}} ) consists only of Hsp60 genes from
Actinopterygii (ray-finned fishes). The ENC values and the
GC\textsubscript{3} content of the Hsp60 genes in cluster \#2 differ
more than in cluster \#1. This feature can be explained by the fact that
Hsp60 from Fish is evolutionarily far from Hsp60 from other Chordata
classes\textsuperscript{14}.

It should be noted that the ENC value of the gene can correlate with the
level of its expression in the cell\textsuperscript{59--62}. Based on
current research, the following ENC thresholds have been established for
determining the level of expression of the Hsp60 gene: ENC \textless{}
40 for highly expressed genes\textsuperscript{59,63,64}; 40 \textless{}
ENC [?] 55 for moderately expressed genes\textsuperscript{59,63,65}; ENC
\textgreater{} 55 for lowly expressed genes\textsuperscript{60,63}.
These conditions can be used to assess the main trends in the average
codon usage bias for Hsp60 genes (Figure 9).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure 9.} Nc-plot of the average codon usage bias in the Hsp60
genes of 17 phyla. The plot space was divided into six quadrants using
the ENC and GC\textsubscript{3} thresholds. The ENC thresholds,
reflecting the level of the Hsp60 gene expression, were as follows: ENC
\textless{} 40 for genes with high expression; 40 \textless{} ENC [?] 55
for moderately expressed genes; ENC \textgreater{} 55 for low expressed
genes. The GC\textsubscript{3} thresholds reflecting the direction of
the mutational pressure were as follows: GC\textsubscript{3} \textless{}
0.5 represents the AT-mutation pressure;
GC\textsubscript{3}\textgreater{} 0.5 represents the GC-mutation
pressure. The average ENC values were grouped according to the obtained
quadrants: Group 1 (Apicomplexa, Firmicutes, and Bacteroidetes); Group 2
(Chlamydiae, Streptophyta, Nematoda, Mollusca, Cyanobacteria, and
Chordata); Group 3 (Euryarchaeota, Arthropoda, and Euglenozoa); Group 4
(Ascomycota, Proteobacteria, Basidiomycota, Chlorophyta, and
Actinobacteria). The black bell-shaped curve represents the expected
effective number of codons (\emph{ENC\textsubscript{exp}} ), i.e.
predicted ENC values if the codon bias is influenced only by the GC
content at the third synonymous position of codons (GC\textsubscript{3})
in the Hsp60 gene. The horizontal dashed lines (ENC=40 and ENC=55)
indicate ENC thresholds for determining the codon usage bias and gene
expression level. The vertical dashed line indicates the
GC\textsubscript{3} content of 0.5. The position of the ENC value
regarding this line indicates the direction of the mutation pressure
affecting the Hsp60 genes (depicted by arrows).

The maximum and minimum average ENC values were observed for
Streptophyta (ENC 51.6\selectlanguage{ngerman}±3.2) and Actinobacteria (ENC 31.6±5.3),
respectively. Using the convention of 40 \textless{} ENC \selectlanguage{english}[?] 55 to
determine the degree of codon usage bias\textsuperscript{60,66,67}, it
can be assumed that Hsp60 genes of 17 phyla are highly and moderately
biased. The Hsp60 genes from Group 1 (Apicomplexa, Firmicutes, and
Bacteroidetes) with AT-mutation pressure and Group 4 (Ascomycota,
Proteobacteria, Basidiomycota, Chlorophyta, and Actinobacteria) with
GC-mutation pressure have a high level of expression. In turn, a
moderate level of Hsp60 gene expression is characteristic of Group 2
(Chlamydiae, Streptophyta, Nematoda, Mollusca, Cyanobacteria, Chordata)
with AT-mutation pressure and Group 3 (Euryarchaeota, Arthropoda, and
Euglenozoa) with GC-mutation pressure.

However, it should be noted that the individual ENC values of the Hsp60
genes from Streptophyta, Mollusca, Chordata, Euryarchaeota, Arthropoda,
Basidiomycota exceed 55 (Figure 7). In addition, there was a high
statistical variance in the average ENC values ranging from 3.2
(Streptophyta) to 8.8 (Apicomplexa) (Supplementary, ENC) for Hsp60 genes
of 17 phyla, which may indicate that overall ENC values are not
conserved for these genes within the phylum. Therefore, the ENC values
were compared using Student's t-test (Figure 10; Supplementary, ENC).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Figure 10.} Symmetric matrix of p-values of the t-test between
the ENC values of the Hsp60 genes from 17 phyla. The statistically
indistinguishable ENC values of Hsp60 genes of the two phyla, having a
t-test with a p-value greater than 0.05, are marked in black.
Statistically different ENC values of the Hsp60 genes of the two phyla,
having a t-test with a p-value less than 0.05, are marked in white. The
black frames and the ``Cluster'' X-axis represent the four clusters and
their numbers (Roman numerals). Clustering was carried out using the
UPGMA algorithm.

According to the results of the t-test (Supplementary, ENC (t-test
results)), the ENC values of Hsp60 genes of 14 phyla (cluster I) are
statistically indistinguishable (Figure 10). In turn, the ENC values for
Actinobacteria (cluster II), Streptophyta (cluster III), and
Proteobacteria (cluster IV) are statistically different from all other
phyla. It can be assumed that the expression level of Hsp60 genes from
Proteobacteria and Actinobacteria is high (34.8 and 31.6, respectively),
while the expression level of the Hsp60 genes from Streptophyta is close
to low (51.6). It should be noted that more accurate conclusions about
the level of Hsp60 gene expression should be made based on the results
obtained by conventional laboratory methods.

\textbf{Patterns of synonymous codon usage for Hsp60 genes}

To understand the peculiarities of codon usage, it is necessary to
assess not only the degree of codon usage bias, but also the
representation of synonymous codons in a gene, or, in other words, the
patterns of synonymous codon usage. The approach widely used for this
purpose is the calculation of synonymous codon usage bias, reflecting
the peculiarities of amino acid coding, when some codons in a gene are
used more often than other synonymous codons\textsuperscript{68}. Under
the influence of the mutational pressure and natural
selection\textsuperscript{69}, the representation of various codons in a
gene plays an important role in the processes of its
transcription\textsuperscript{70} and translation\textsuperscript{71}.
Determination of patterns of synonymous codon usage is necessary for
understanding the features of gene/genome evolution. Thus, the relative
synonymous codon usage (RSCU) of 64 codons in the Hsp60 genes was
calculated (Figure 11; Supplementary, RSCU).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\textbf{Figure 11.} Average values of relative synonymous codon usage
(RSCU) for Hsp60 genes from 17 phyla. The RSCU values for each of 17
phyla were divided into two main groups according to the type of base at
the third synonymous position of codon: a - A/T-ending codons; b -
G/C-ending codons. Phyla were divided into three groups by the direction
of the mutational pressure (see Table 2) and sorted by the average total
GC content of Hsp60 genes. The codons were sorted by the average RSCU
value between 17 phyla.

The RSCU values of A/T-ending codons were higher for the Hsp60 genes
under the influence of AT mutation pressure, and \emph{vice
versa}(Figure 11), which was to be expected\textsuperscript{72}. The
RSCU values greater than 1.5 and less than 0.5 correspond to high and
low represented codons, respectively. Among the stop codons, TAA is the
most widely represented stop codon with an average RSCU value of
1.8\selectlanguage{ngerman}±0.7. In turn, TGA codon is widely used in the Hsp60 genes of
Euglenozoa and Actinobacteria with the average RSCU values of 1.8±1.6
and 1.8±1.5, respectively. This may be due to the direction of the
mutational pressure affecting the Hsp60 genes of these phyla, since with
an increase in the GC content, the frequency of TGA codon usage
increases\textsuperscript{73}. This may also explain the decrease in the
average RSCU values for TAA for GC-biased phyla (Figure 11).

To assess the main trends in the synonymous codon usage in Hsp60 genes
from phyla with different directional mutation pressure, summary
patterns of RSCU values were compiled (Figure 12).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\textbf{Figure 12.} Summary patterns of relative synonymous codon usage
for Hsp60 genes being under the different mutational pressure. The
average RSCU values of Hsp60 genes from phyla with the AT, AT/GC, and GC
mutational pressure were divided into two main groups according to the
type of base at the third synonymous position of codon: a - A/T-ending
codons; b - G/C-ending codons. The codons were sorted by the average
RSCU value between all 17 phyla.

As can be seen, the number of high and low represented codons that
correspond to phyla under the GC mutational pressure is greater than for
AT and AT/GC-biased phyla. Such results for AT/GC-biased phyla can be
explained by their GC content at the border between the AT and GC
mutational pressure groups (Table 2). Codons encoding Ala, Arg, Gly,
Ile, Leu, Ser, Thr, and Val were widely represented for almost all Hsp60
genes. For each of 17 phyla, the set of these codons is consistent with
the direction of the mutational pressure (predominance of A/T- or
G/C-ending codons) affecting the Hsp60 genes. CGA (R), GGG (G), and TAG
(stop) codons have the lowest average RSCU values for all 17 phyla (less
than 0.5) regardless of the direction of the mutational pressure.

Taking these results into account, it can be assumed that with an
increase in the GC content in the Hsp60 gene due to mutations at the
third synonymous codon position, the selectivity of codon usage
increases.

\textbf{Conclusion}

In the present study, a comprehensive analysis of the amino acid and
nucleotide Hsp60 sequences from 19 phyla was carried out. For this,
databases 1 and 2 were built, consisting of 19220 amino acid and 1925
nucleotide Hsp60 sequences, respectively.

Multiple alignment, calculation of the percent identity (PID) of amino
acid sequences from database 1, and subsequent clustering of the
obtained data were performed to establish the level of conservatism of
Hsp60. It turned out that Hsp60 cannot be considered a highly conserved
protein, since the average PID values vary widely from 10.1\selectlanguage{ngerman}±0.5\%
(Chlamydiae \#1 / Apicomplexa \#2) to 97.9±0.0\% (Mollusca \#2 /
Mollusca \#3). However, the result of component analysis indicates a
relatively constant amino acid composition of Hsp60, showing a high
content of aliphatic (Ala, Val, Gly, Leu, and Ile), charged (Glu, Lys,
and Asp), and polar (Thr) amino acid residues. Thus, it can be assumed
that the functional features of Hsp60 are determined not only by its
sequence, but also by its amino acid composition.

The nucleotide sequences from database 2 were analyzed to determine the
genetic and evolutionary characteristics of Hsp60 genes from 17 phyla
using conventional metrics. The GC content of the analyzed Hsp60 genes
was comparable to or higher than the corresponding genomic values. It
can be assumed that the Hsp60 genes are tightly controlled by DNA repair
systems, providing high resistance to spontaneous mutations in the third
position of codons and thereby increasing their GC content. It was
further found that natural selection plays a dominant role in the
evolution of Hsp60 genes. According to the results of the neutrality
plot analysis, the percent of impact of mutational pressure on the codon
usage of Hsp60 genes in 16 phyla does not exceed 20\%, with the
exception of Euryarchaeota, for which Hsp60 genes are characterized by
high mutational pressure with neutrality values of 32.4\%. In addition,
the direction of mutational pressure affecting the third position of
codons was determined. Accordingly, the Hsp60 genes from Apicomplexa,
Chlamydiae, Firmicutes, Streptophyta, Nematoda, Bacteroidetes, Mollusca,
and Cyanobacteria are under AT mutational pressure. In turn, GC
mutational pressurized Hsp60 genes belong to Euryarchaeota, Ascomycota,
Euglenozoa, Basidiomycota, Chlorophyta, and Actinobacteria phyla. It
should be noted that the Hsp60 genes from Chordata, Arthropoda, and
Proteobacteria phyla cannot be assigned to any of these groups. However,
further division by class showed an interesting result for Chordata. The
Hsp60 genes from Fish were found to be under GC mutational pressure,
while Hsp60 genes from other classes were AT-biased. Also noteworthy are
four representatives of Hyperoartia, Mammalia, and Leptocardii classes,
whose Hsp60 genes belonging to the Fish's subgroup. This feature may be
due to the fact that they are all aquatic animals, which makes them
related to Fish.

The values of effective number of codons (ENC) and relative synonymous
codon usage (RSCU) were used to assess codon usage and level of codon
bias. According to the ENC values, a moderate or high level of Hsp60
gene expression was observed, which is evident since Hsp60 is a
ubiquitous protein. At the same time, the direction of mutational
pressure in the Hsp60 genes did not affect the size of a ``codon
dictionary'' that is used to encode genes. However, the results of the
synonymous codons bias analysis showed that the average RSCU values for
A/T-ending codons were higher for Hsp60 genes under AT mutation pressure
and \emph{vice versa} . TAA codon was the most preferred stop codon for
the Hsp60 genes. Using division by the direction of mutational pressure
and the average RSCU values for Hsp60 genes from these groups, it was
found that the number of high (RSCU\textgreater{}1.5) and low
(RSCU\textless{}0.5) represented codons for the Hsp60 genes under GC
mutation pressure is greater, than for AT-biased Hsp60 genes. It can be
assumed that an increase in the GC content of Hsp60 genes ensures the
optimization of codon usage.

Thus, the present study demonstrates that Hsp60 is a protein inherent in
all living organisms, characterized by a relatively constant amino acid
composition and low sequence conservatism, the feature of which is the
dominance of natural selection forces in evolution of the gene and its
high resistance to spontaneous mutations in the third synonymous
position of codons.

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\textbf{Figure captions}

\textbf{Figure 1.} Symmetric matrix of the average PID values. The
matrix contains 60 sub-clusters of Hsp60 sequences from 19 phyla. The X-
and Y-axis items ``Sub-cluster'' are represented in the following format
``Phylum \#sub-cluster (number of sequences in a sub-cluster)''.
Sub-clusters of Viruses have no phylum labels. The Y-axis ``Kingdom''
represents sub-clusters united by a higher taxonomic rank (Kingdom). The
black frames and the X-axis ``Cluster'' show four clusters and their
numbers (Roman numerals), which were obtained by clustering 60
sub-clusters. Clustering was performed using the UPGMA algorithm.

\textbf{Figure 2.} Heat map showing normalized average PID values
between Hsp60 sequences belonging to sub-clusters Arthropoda \#2,
Arthropoda \#3, and Nematoda \#1 and Hsp60 sequences belonging to each
of 19 phyla. The average PID values were normalized using the min-max
normalization method. The 19 phyla were sorted by NCBI Taxonomy.

\textbf{Figure 3.} The average amino acid composition of the Hsp60
sequences from 19 phyla: a - Heat map displaying normalized average PID
values between 19 phyla of Hsp60 sequences; b - The average amino acid
composition of the Hsp60 sequences for each of the 19 phyla compared to
the corresponding proteomic values. In Figure 3a the average values were
normalized using the min-max normalization method. The line ``Summary''
presents the average normalized amino acid composition of Hsp60 for 19
phyla. In Figure 3b the amino acid profiles were represented as the
average amino acid composition of Hsp60 for each of 19 phyla compared to
the average amino acid composition of the respective proteomes. The
structure of color scale is as follows: Higher/Lower - the amino acid
content in Hsp60 is higher/lower than in proteomes, respectively;
Comparable - the amino acid content in Hsp60 is comparable to the
average proteomic value. The ``Summary'' line shows the average amino
acid profile of Hsp60 for 19 phyla. The groups were sorted using the
NCBI Taxonomy. Amino acids were sorted using an average amino acid
composition of 19220 Hsp60 sequences.

\textbf{Figure 4.} The average nucleotide composition of the Hsp60 genes
from 17 phyla: a -- The average total GC contents at each positions of
codon of Hsp60 sequences and corresponding genomes; b - The average
content of GC\textsubscript{1}, GC\textsubscript{2}, and
GC\textsubscript{3} in Hsp60 genes. Phyla were sorted by average total
GC content of Hsp60 sequences. Student's t-test was used to compare the
average GC content of the Hsp60 sequences and the average GC content of
the corresponding genomes. The difference between two independent
samples of GC values is considered statistically significant if the
p-value is less than 0.05. Statistically indistinguishable average GC
values are marked with ``ns'' (non-significant).

\textbf{Figure 5.} Neutrality plots (GC\textsubscript{1,2} vs.
GC\textsubscript{3}) for Hsp60 genes from 17 phyla. The
GC\textsubscript{1,2} values represent the average GC content at the
first and second positions of codon (GC\textsubscript{1} and
GC\textsubscript{2}), while GC\textsubscript{3} values represent the GC
content at the third synonymous codon position. The solid line
represents the linear regression of GC\textsubscript{1,2} versus
GC\textsubscript{3}, the correlation of which is described by the
regression coefficient R and its p-value. The correlation coefficient R
reflects the strength of the impact of GC\textsubscript{3} on
GC\textsubscript{1,2}. The p-value characterizes the significance of R.
Changes in GC\textsubscript{3} values actually affect the
GC\textsubscript{1,2} values when the p-value of R is less than 0.05. In
turn, changes in GC\textsubscript{3} are considered random, and the R
coefficient is not irrelevant when the p-value is greater than 0.05,
i.e. GC\textsubscript{3} and GC\textsubscript{1,2} values are not
correlated. The slope \selectlanguage{greek}\emph{ε} \selectlanguage{english}of the regression line indicates the
neutrality of the codon usage. Neutrality values were determined by
equation {[}\selectlanguage{greek}\emph{ε} \selectlanguage{ngerman}× 100, \%{]}. Slope values ranging from 0 to 1 were
calculated using the least-squares regression analysis. The dashed line
is a complete neutrality plot, which reflects the complete equilibrium
of the nucleotide composition of the gene/genome with directional
mutation pressure. The equilibrium point \emph{E\textsubscript{p}} was
defined as the intersection point of the neutrality plot (regression
line) and the complete neutrality plot. The
\emph{E\textsubscript{p}}value reflects the GC\textsubscript{3} content
of the gene/genome when the mutation frequencies (AT\selectlanguage{english}-GC and GC\selectlanguage{english}-AT) are
equal. The direction of the mutational pressure, indicating an imbalance
in the frequencies of the AT\selectlanguage{english}-GC and GC\selectlanguage{english}-AT mutations, was determined in
accordance with the following conditions: the average GC content value
less than the\emph{E\textsubscript{p}} value reflects the AT mutational
pressure; the average GC content value greater than the
\emph{E\textsubscript{p}} value reflects the GC mutational pressure.
Phyla were sorted by average total GC content of Hsp60 genes.

\textbf{Figure 6.} Neutrality plot for Hsp60 genes of Chordata

\textbf{Figure 7.} Nc-plots of codon usage bias in Hsp60 genes from the
17 phyla. Gray scatter plots represent ENC values versus
GC\textsubscript{3} content for Hsp60 genes from 17 phyla. The black
bell-shaped curves represent the expected effective number of codons
(\emph{ENC\textsubscript{exp}} ), i.e. predicted ENC values if codon
usage bias is influenced by GC\textsubscript{3} content (GC content at
the third synonymous position of codons) in the Hsp60 gene only. Phyla
were sorted by the average total GC content of Hsp60 genes.

\textbf{Figure 8.} Clustering of ENC values for Hsp60 genes from
Chordata. Cluster \#1 includes the ENC values of Hsp60 genes of
Mammalia, Aves, Reptilia, and Amphibia. Cluster \#2 includes ENC values
of Hsp60 genes of Fish. Clustering was performed using the value of the
GC\textsubscript{3} content corresponding to the equilibrium
point\emph{E\textsubscript{p}} , which was determined earlier (see
GC-content and mutation pressure for codon usage).

\textbf{Figure 9.} Nc-plot of the average codon usage bias in the Hsp60
genes of 17 phyla. The plot space was divided into six quadrants using
the ENC and GC\textsubscript{3} thresholds. The ENC thresholds,
reflecting the level of the Hsp60 gene expression, were as follows: ENC
\textless{} 40 for genes with high expression; 40 \textless{} ENC \selectlanguage{english}[?] 55
for moderately expressed genes; ENC \textgreater{} 55 for low expressed
genes. The GC\textsubscript{3} thresholds reflecting the direction of
the mutational pressure were as follows: GC\textsubscript{3} \textless{}
0.5 represents the AT-mutation pressure;
GC\textsubscript{3}\textgreater{} 0.5 represents the GC-mutation
pressure. The average ENC values were grouped according to the obtained
quadrants: Group 1 (Apicomplexa, Firmicutes, and Bacteroidetes); Group 2
(Chlamydiae, Streptophyta, Nematoda, Mollusca, Cyanobacteria, and
Chordata); Group 3 (Euryarchaeota, Arthropoda, and Euglenozoa); Group 4
(Ascomycota, Proteobacteria, Basidiomycota, Chlorophyta, and
Actinobacteria). The black bell-shaped curve represents the expected
effective number of codons (\emph{ENC\textsubscript{exp}} ), i.e.
predicted ENC values if the codon bias is influenced only by the GC
content at the third synonymous position of codons (GC\textsubscript{3})
in the Hsp60 gene. The horizontal dashed lines (ENC=40 and ENC=55)
indicate ENC thresholds for determining the codon usage bias and gene
expression level. The vertical dashed line indicates the
GC\textsubscript{3} content of 0.5. The position of the ENC value
regarding this line indicates the direction of the mutation pressure
affecting the Hsp60 genes (depicted by arrows).

\textbf{Figure 10.} Symmetric matrix of p-values of the t-test between
the ENC values of the Hsp60 genes from 17 phyla. The statistically
indistinguishable ENC values of Hsp60 genes of the two phyla, having a
t-test with a p-value greater than 0.05, are marked in black.
Statistically different ENC values of the Hsp60 genes of the two phyla,
having a t-test with a p-value less than 0.05, are marked in white. The
black frames and the ``Cluster'' X-axis represent the four clusters and
their numbers (Roman numerals). Clustering was carried out using the
UPGMA algorithm.

\textbf{Figure 11.} Average values of relative synonymous codon usage
(RSCU) for Hsp60 genes from 17 phyla. The RSCU values for each of 17
phyla were divided into two main groups according to the type of base at
the third synonymous position of codon: a - A/T-ending codons; b -
G/C-ending codons. Phyla were divided into three groups by the direction
of the mutational pressure (see Table 2) and sorted by the average total
GC content of Hsp60 genes. The codons were sorted by the average RSCU
value between 17 phyla.

\textbf{Figure 12.} Summary patterns of relative synonymous codon usage
for Hsp60 genes being under the different mutational pressure. The
average RSCU values of Hsp60 genes from phyla with the AT, AT/GC, and GC
mutational pressure were divided into two main groups according to the
type of base at the third synonymous position of codon: a - A/T-ending
codons; b - G/C-ending codons. The codons were sorted by the average
RSCU value between all 17 phyla.

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