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Article

Domestication Explains Two-Thirds of Differential-Gene-Expression Variance between Domestic and Wild Animals; The Remaining One-Third Reflects Intraspecific and Interspecific Variation

by
Irina Chadaeva
,
Petr Ponomarenko
,
Rimma Kozhemyakina
,
Valentin Suslov
,
Anton Bogomolov
,
Natalya Klimova
,
Svetlana Shikhevich
,
Ludmila Savinkova
,
Dmitry Oshchepkov
,
Nikolay A. Kolchanov
,
Arcady Markel
and
Mikhail Ponomarenko
*
Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
*
Author to whom correspondence should be addressed.
Animals 2021, 11(9), 2667; https://doi.org/10.3390/ani11092667
Submission received: 19 August 2021 / Revised: 4 September 2021 / Accepted: 6 September 2021 / Published: 10 September 2021
(This article belongs to the Section Animal Genetics and Genomics)
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Abstract

:

Simple Summary

Before genomes were sequenced, zoologists had discovered destabilizing selection as a general pattern of animal domestication that in foxes and minks had yielded fur colors never seen in the wild. Today, known genomes of humans and domestic and wild animals arouse interest in a common whole-genome pattern of animal domestication that may at least explain differences between the effects of natural and artificial environments on organisms. Accordingly, here we identified genes differing in expression between tame and aggressive rats (a laboratory domestication model) to compare them with all such known changes of genes’ activity (available in databases) in other domestic versus wild animals (e.g., dogs versus wolves). The results show that the majority of these genes changed their expression similarly among all the domestic versus wild animals studied, i.e., explained two-thirds of the variance, while the remaining one-third reflected animal intraspecific and interspecific variation, just as the gold standard of variation in nature. Accordingly, here we found that the artificial environment of domestic animals alters activities of genes in the same direction as that seen in corresponding human genes during human diseases, whereas the natural environment maintains a normal gene expression pattern in wild animals (matching human health).

Abstract

Belyaev’s concept of destabilizing selection during domestication was a major achievement in the XX century. Its practical value has been realized in commercial colors of the domesticated fox that never occur in the wild and has been confirmed in a wide variety of pet breeds. Many human disease models involving animals allow to test drugs before human testing. Perhaps this is why investigators doing transcriptomic profiling of domestic versus wild animals have searched for breed-specific patterns. Here we sequenced hypothalamic transcriptomes of tame and aggressive rats, identified their differentially expressed genes (DEGs), and, for the first time, applied principal component analysis to compare them with all the known DEGs of domestic versus wild animals that we could find. Two principal components, PC1 and PC2, respectively explained 67% and 33% of differential-gene-expression variance (hereinafter: log2 value) between domestic and wild animals. PC1 corresponded to multiple orthologous DEGs supported by homologs; these DEGs kept the log2 value sign from species to species and from tissue to tissue (i.e., a common domestication pattern). PC2 represented stand-alone homologous DEG pairs reversing the log2 value sign from one species to another and from tissue to tissue (i.e., representing intraspecific and interspecific variation).

1. Introduction

One of the key achievements of genetics in the XX century is Belyaev’s concept of destabilizing selection during animal domestication as the most common pattern of domestication, namely: “In a genetic and biochemical sense, what may be selected for are changes in the regulation of genes —that is, in the timing and the amount of gene expression rather than changes in individual structural genes. Selection having such an effect is called by me destabilizing selection. The selection becomes destabilizing when it affects, directly or indirectly, the systems of neuroendocrine control of ontogenesis. This seems always to be the case when some new stressful factors appear in the environment, or when stresses usual for the species increase in strength.” [1]. This breakthrough summed up the results of long-term unique experiments on both mink (e.g., [2]) and fox (e.g., [3]) domestication as well as on mice as a laboratory model of human diseases (e.g., [4]). Among these experiments was our study on how emotionally mice respond to stress [5]. The practical value of Belyaev’s concept has been realized in a huge collection of commercial fur colors—e.g., “Amber-gold pastel,” “Ashen,” “Beige,” “Black crystal,” “Cobalt,” “Ermine-like,” “Peach,” ““Pearl,” Platinum,” “Purple,” “Silver sable-like,” “Steel-blue,” and “Straw,”—rarely or never seen in the wild [6]. The concept’s fundamental importance has found confirmation in a large variety of breeds of dogs [7], cats [8], pigs [9], cows [10], horses [11], sheep [12], goats [13], chickens [14], ducks [15], geese [16], and other domestic animals [17] as well as in artificial shelters and additional feeding for wild animals in wildlife for saving them [18]. On the basis of Belyaev’s concept [1], a laboratory model of animal domestication by humans has been created using outbred lines of tame and aggressive rats artificially bred [19] for performance on a standard glove test [20]. Moreover, within translational biomedicine [21], a lot of the human disease models involving domestic and laboratory animals, including transgenic ones [22], are actively researched to eventually conduct preclinical trials of drugs intended for human treatments. Perhaps that is why the majority of transcriptome-profiling studies on domestic versus wild animals are still focused on the search for the species specificity of differentially expressed genes (DEGs) that has practical value [23,24,25,26,27,28,29] rather than for common patterns of domestication. An exception from the mainstream in genome-wide studies on animals is a comparison of the human variome with differences between domestic and wild animals, where the researchers have generalized their results by means of the new concept of self-domestication syndrome, the symptoms of which include autism spectrum disorders [30], although the idea of human self-domestication is still debatable [31].
Among the studies fitting Belyaev’s concept of destabilizing selection during domestication [1], in our previous works, we have measured the murine emotional response under stress [5]. Next, our genome-wide study of the CpG-islands within gene promoters in humans, chimpanzees, gorillas, and orangutans identified the evolution of brain active promoters in the human lineage toward their increased epigenetic regulation plasticity [32]. Moreover, we have conducted a genome-wide study on single-nucleotide polymorphisms (SNP) within gene promoters of the human nervous system and found that natural selection equally supports propensities to domination and subordination, which must be inherited from parent to offspring; we proved this idea using F1 hybrid mice [33]. Additionally, by quantitative polymerase chain reaction (qPCR), we have identified seven hypothalamic DEGs of tame versus aggressive rats [34] as the above-mentioned laboratory model of animal domestication [19,20]. Finally, applying the factor analysis [35] to these seven rat DEGs, we for the first time observed that artificial selection for behavior, adaptation to laboratory living conditions, and intraspecific variety equally well explain the differential-gene-expression variance of tame versus aggressive rats [36]. That is why the present work generalizes our above-mentioned qPCR-based findings at the whole-genome scale. Biomedical applications of the results are discussed at the end of the paper.

2. Materials and Methods

2.1. Animals

The study was performed on adult male gray rats (Rattus norvegicus) selectively bred for over 90 generations for either aggressive or domesticated behavior (as two outbred lines) under standard conditions of the Conventional Animal Facility of the Institute of Cytology and Genetics (ICG), the Siberian Branch of the Russian Academy of Sciences (SB RAS; Novosibirsk, Russia), as described elsewhere [19,20,37]. The total number of rats was 22 (11 aggressive and 11 domesticated), each weighing 250–270 g and 4 months old, all from different unrelated litters. All the rats were decapitated. Following a handbook [38], we extracted samples of the hypothalamus, which were then flash-frozen in liquid nitrogen and stored at −70 °C until use. We focused on the hypothalamus as a universal brain region most frequently used in studies on aggressiveness of female and male animals of all ages (for review, see e.g., [39]). Every effort was made to minimize the number of animals studied and their suffering.

2.2. RNA-Seq

For RNA isolation, approximately 100 mg of tissue was used. Total RNA from hypothalamus samples of tame (n = 3) and aggressive (n = 3) rats for sequencing was isolated using the TRIzol™ reagent (Invitrogen, Carlsbad, CA, USA, cat. #15596018) according to the manufacturer’s protocol. By means of the mirVana™ Kit (miRNA Isolation Kit without phenol, Thermo Fisher Scientific, AB-AM1561), the RNA was separated. The total RNA after separation on mirVana columns was analyzed quantitatively by means of an Invitrogen Qubit™ 2.0 fluorometer (Invitrogen/Life Technologies, Carlsbad, CA, USA).
The quality of total RNA was assessed with the RNA Nano Kit on an Agilent Bioanalyzer 2100 bioanalyzer (Agilent, Santa-Clara, CA, USA). Samples with RNA Integrity Numbers (RINs) of 7.0 to 8.0 were chosen for further analysis. To obtain a highly purified mRNA fraction, the Dynabeads mRNA Purification Kit (Invitrogen) was employed in accordance with the manufacturer’s protocol. For purification, 5 μg of the RNA fraction depleted of small RNAs was used. The absence of significant degradation of RNA and the presence of rRNA impurities at less than 20% were monitored on a bioanalyzer with the RNA Pico Kit. To create barcoded RNA-Seq libraries, 15–30 ng of mRNA was used; the ScriptSeq™ v2 RNA-Seq Library Preparation Kit (epicenter®, Madison, WI, USA) was utilized according to the manufacturer’s protocol. When amplifying the libraries, we carried out 13 polymerase chain reaction (PCR) cycles; the final purification was performed on AMPure XP magnetic beads. The quality of the obtained libraries and their molarity were checked on a bioanalyzer using the DNA High Sensitivity kit; before loading, the library was diluted 1:10. The molarity of the 24 obtained libraries was in the range of 10,000–47,000 pmol/L. The libraries were pooled in equimolar amounts, adjusted to a concentration of 2 nM, and transferred to the Multi-Access Center of Genomic Research (ICG SB RAS, Novosibirsk, Russia) for sequencing on an Illumina NextSeq 550 instrument in a NextSeq® 500/550 High Output Kit v2 cassette (75 cycles) under the assumption of a direct read of 75 nucleotides, with at least 40 million reads. The total volume of sequencing for the 6 libraries was 219,086,104 reads, which were finally deposited in the NCBA SRA database (ID = PRJNA668014) [40].

2.3. Mapping of RNA Sequences to the R. norvegicus Reference Genome

The quality of the obtained raw Fastq files was checked and analyzed with FastQC. To improve the quality of the raw reads, we employed the Trimmomatic tool [41] by these procedures: removing a base from either the start or end position if the quality was low, trimming bases by a sliding-window method, and removing any remaining reads that are <36 bases long. The trimmed reads were aligned to the annotated R. norvegicus genome retrieved from the UCSC database (RGSC Rnor_6.0, UCSC version Rn6, July 2014 assembly). Alignment was performed in TopHat2 [42]. The alignments were postprocessed into sorted BAM files by means of SAMTools version 1.4 [43]. Reads were attributed to genes using the htseq-count tool from the “HTSeq” framework version 0.7.2 [44] based on gtf-files with coordinates of genes from Rnor_6.0 and an indexed SAM file. Differential expression analysis was performed in DESeq2 [45] on the IRIS web server, which is publicly available at http://bmbl.sdstate.edu/IRIS/ (accessed on 16 January 2020). Genes were considered differentially expressed if they showed an adjusted p value of less than 0.05 to ensure statistical significance [46], as widely accepted in the case of DEGs in domestic vs. wild animals (e.g., [23,25,29]).

2.4. qPCR

As independent selective verification of the DEGs identified using the above-described RNA-Seq (see Section 2.2 and Section 2.3), in this work, we for the first time examined the total RNA from only the remaining hypothalamus samples of tame (n = 8) and aggressive (n = 8) rats, which was intended only for this qPCR control assay. That total RNA was also isolated with TRIzol and then purified on Agencourt RNAClean XP Kit magnetic beads (Beckman, #A63987). The amount of RNA was evaluated on a Qubit™ 2.0 fluorometer (Invitrogen/Life Technologies) with a reagent kit (RNA High Sensitivity, Invitrogen # In = Q32852) according to the manufacturer’s instructions. cDNA was synthesized with Reverse Transcription Kit reagents (Syntol, #OT-1). Oligonucleotide primers for qPCR were designed in PrimerBLAST [47], as shown in Table 1. qPCR was conducted with the EVA Green I Kit following the manufacturer’s instructions. The qPCR was carried out in three technical replicates on LightCycler® 96 (Roche, Basel, Basel-Stadt, Switzerland). The efficiency of the qPCR was determined by means of a series of cDNA dilutions (standards). Four rat genes—B2m (β-2-microglobulin), Hprt1 (hypoxanthine phosphoribosyltransferase 1), Ppia (peptidylprolyl isomerase A), and Rpl30 (ribosomal protein L30)—served as reference genes according to published recommendations [48,49,50,51].
Finally, using these qPCR-based magnitudes together with the standard software Statistica (StatsoftTM, Tulsa, OK, USA), we selectively verified the aforementioned RNA-Seq data (see Section 2.2) by both the Mann–Whitney U test and Fisher’s Z-test as well as both linear and rank correlations.

2.5. DEGs of Domestic Animals versus Their Wild Congeners

In this work, we utilized publicly available independent experimental RNA-Seq datasets from transcriptomes of domestic animals compared with their wild congeners [23,24,25,26,27,28,29]; these data were statistically significant according to Fisher’s Z-test, with corrections for multiple comparisons (PADJ < 0.05), as published by their authors (see, the PubMed database [52]) and outlined in Table 2.
As a result, 2347 DEGs were analyzed, which represented seven tissues of seven domestic animal species and seven of their wild congeners, as readers can see in in the last row of Table 2. Figure 1 represents an algorithmic flowchart detailing the verification of the DEGs found in this work in the hypothalamus of tame versus aggressive rats with respect to their known homologous DEGs in domestic animals versus their wild congeners, as independently reported by others (Table 2), as depicted by the “TEST DATA” area. In brief, we first searched for homologous genes of rats and other animals among all analyzed genes (STEP 1). Next, applying a principal component analysis to these pairwise expression-fold-change combinations of genes-homologs, we found two principal components PC1 and PC2 (STEP 2). Then, we conducted a statistical analysis of the correlation between either multiple-homolog orthologous DEGs or single orthologous DEGs, namely, we tested whether there are the same (PC1) or opposite (PC2) signs of the log2-transformed ratio of an expression level of a given gene in tame rats to that in aggressive rats (hereinafter: log2-value) within an orthologous DEG pair being tested (STEP 3). Finally, using both linear and rank correlation analysis, we found significant positive correlations between fold changes of the multiple homologous DEGs (PC1) and significant negative correlations between fold changes of the only single homologous DEGs (PC2); this is STEP 4 in Figure 1.

2.6. Statistical Analysis

As depicted in Figure 1, during our statistical analysis, we compiled standard statistical 2 × 2 tables, which served as input data for the standard software Statistica (StatsoftTM, Tulsa, OK, USA) in its pipeline “Statistics” → “Nonparametric” → “Table 2 × 2.” This brought us to Fisher’s exact test, Pearson’s χ2 test, and binomial distribution analysis to test the significance of the results (Figure 1). Likewise, by means of the same toolbox with proper options, we carried out the Mann–Whitney U test, Fisher’s Z-test (see Section 2.4), and principal component analysis (Figure 1).

3. Results

3.1. RNA-Seq and Mapping to the Reference Rat Genome

Using an Illumina NextSeq 550 system, we sequenced the hypothalamus transcriptome of three tame adult male rats and that of three aggressive ones. The rats had no family relations and represented two outbred lines (see Section 2). From 219,086,104 raw reads (75 nt, deposited in the NCBI, PRJNA668014) 184,991,379 reads (84%) were mapped to reference genome Rn6 (Table 3). This allowed us to identify 14,039 genes expressed within the hypothalamus of adult male rats under the experimental conditions used (Table 3). According to Fisher’s Z-test, 1025 of these genes (7%) were statistically significantly differentially expressed between tame and aggressive rats in their hypothalamus at the commonly accepted confidence threshold p < 0.05, as presented in Table 3. To minimize false positive error rates, we applied the Benjamini correction for multiple comparisons, which finally resulted in 46 DEGs in the hypothalamus of the tame versus aggressive rats under study (Table 3); the DEGs are listed in Table 4. In particular, rat gene Ascl3 encoding achaete-scute family bHLH transcription factor 3 turned out to be the best DEG within the hypothalamus of the tame versus aggressive rats owing to its smallest PADJ value, 10−8. This DEG has a log2 value of 3.99, as shown in Table 4, row #1.

3.2. qPCR Selective Verification of the DEGs Identified in this Work in the Hypothalamus of Tame versus Aggressive Rats

For this purpose, we selected three out of the 46 DEGs listed in Table 4, namely, Ascl3 (i.e., the above-mentioned best DEG), Apobec1, and Defb17, as shown in Table 5.
Additionally, separately, we prepared eight other tame adult male rats and eight other aggressive ones (all unrelated), who scored either “−3” or “3” (Table 5) on a scale from −4 (the most aggressive rat) to 4 (the tamest rat) in the standard glove test [20]. It was performed 1 month before the extraction of hypothalamus samples to minimize the effects of the glove test on the results of this study (see Section 2). At the bottom of Table 5, we present our qPCR data on the three DEGs examined in the hypothalamus of the aggressive versus tame rats as mean ± standard error of mean (M0 ± SEM) of their relative expression with respect to four reference genes B2m, Hprt1, Ppia, and Rpl30 on the basis of three technical replicates. These qPCR values (expression levels) varied from 0.01 to 9.22. In three out of eight aggressive rats (#3, #4, and #5), Defb17 expression levels in the hypothalamus were below the threshold of sensitivity under the experimental conditions where we reliably measured the expression of all the tested genes in all studied rats (except for these three cases, as indicated by “ND, not detected” in Table 5).
Moreover, in the rightmost column of Table 5, we present the results of averaging the expression levels of each of the three verified DEGs in the hypothalamus of the aggressive and tame rats examined. Figure 2a shows graphical representation of these qPCR results.
In this figure, readers can see statistically significant overexpression of all the three examined DEGs in the hypothalamus of the tame male adult rats (white bars) compared with aggressive ones (grey bars) according to the qPCR data obtained in this work, in agreement with the expression levels detected in the transcriptome analysis (Table 4), as depicted by asterisks, each of which means p < 0.05 in both the Mann–Whitney U test and Fisher’s Z-test, which are independent from one another.
Figure 2b presents statistically significant correlations between relative expression levels in the hypothalamus of tame versus aggressive rats, as measured experimentally by two methods [RNA-Seq (X-axis) and qPCR (Y-axis)] independent from each other, with the results expressed in log2 values (see “Materials and methods”) for the six genes, namely: three selected DEGs (i.e., Ascl3, Apobec1, and Defb17) and three reference genes (i.e., B2m, Ppia, and Rpl30) as depicted by open circles. In this figure, solid and dash-and-dot lines represent linear regression and boundaries of its 95% confidence interval, as calculated by means of software package Statistica (StatsoftTM, Tulsa, OK, USA). As one can see, the coefficients of Pearson’s linear correlation (r = 0.89, p < 0.001), Spearman’s rank correlation (R = 1.00, p < 0.05), Kendall’s rank correlation (τ = 1.00, p < 0.005), and Goodman–Kruskal generalized correlation (γ = 1.00, p < 0.005) are statistically significant, while being independent from one another.
Moreover, the filled circles depict seven statistically significant DEGs in the hypothalamic samples of eight other tame rats versus eight other aggressive rats according to a qPCR-based identification protocol published in our previous work [34]. These RNA-Seq [this work] and qPCR [34] data are given in Table S1, where their Pearson’s linear correlation is statistically significant too, namely: r = 0.71 at p < 0.05 (hereinafter, see Supplementary Materials). Moreover, as readers can see here, all the seven independently identified DEGs do not go beyond the boundaries of the 95% confidence interval of the linear regression under consideration (i.e., they are between the two dash-and-dot lines).
Summing Figure 2 up, both the nonparametric Mann–Whitney U test and parametric Fisher’s Z-test as well as Pearson’s linear correlation, Goodman–Kruskal generalized correlation, and Spearman’s and Kendall’s rank correlations taken together independently indicate that in this study, the qPCR data on the hypothalamus of eight tame adult male rats compared with eight aggressive ones statistically significantly confirm the DEGs found in the hypothalamus of three other tame adult male rats versus three other aggressive ones (all unrelated animals). This confirmation means statistical robustness of the DEGs identified in this work.

3.3. Verification of the DEGs Found Here in the Hypothalamus of Tame versus Aggressive Rats with Respect to Their Known Homologous DEGs in Domestic and Wild Animals (All Data That We Could Find)

In this study, we for the first time verified the DEGs identified by us in the hypothalamus of tame versus aggressive rats with respect to their known homologous DEGs in domestic animals compared with their wild congeners as reported by others (Figure 1 and description in Section 2). With this in mind, to the 46 above-mentioned hypothalamic DEGs of tame versus aggressive adult male rats (Table 4), we first of all added 14 independent publicly available RNA-Seq datasets on domestic versus wild animals [23,24,25,26,27,28,29], entitled “TEST DATA” in Figure 1 and characterized in Table 2. There, to minimize false positive error rates, we took into account only the statistically significant DEGs (according to the Benjamini correction for multiple comparisons) published in relevant articles cited in the rightmost column of Table 2. This procedure eventually resulted in 2347 DEGs in seven tissues of seven pairs of domestic versus wild cognate animals (Table 2: bottom row).
As depicted by a Venn diagram in Figure 1 (STEP 1), next, we managed to compile 54 pairs of homologous DEGs (listed in Table 6), where each pair contains one DEG taken from Table 4 (i.e., columns i to iv of Table 6) as well as its homologous DEG chosen from the 2347 DEGs characterized in Table 2 as detailed in columns v to xi of Table 6.
Following our previous article [36] on a factor analysis of qPCR-identified DEGs within in the hypothalamus of tame versus aggressive rats [34], in this work, we processed Table 6 by principal component analysis (Figure 1: STEP 2), the results of which are presented in Figure 3. In this figure, one can see that the first (main) principal component PC1 (X-axis) is proportional to arithmetic means of the log2 values measured by independent experiments on domestic animals compared with their wild congeners. PC1 explains two-thirds (67%) of differential-gene-expression variance under study. This is parallel to the dotted line (Figure 3) along which all orthologs (Banp, Cd22, Nr4a3, and Hbb-b1) with multiple homologs are located. Indeed, two Banp-related orthologous gene pairs (#1 and #2) supported one another in homology, as did two Cd22-related gene pairs (#3 and #4). Additionally, two Nr4a3-related gene pairs (#29 and #30) were supported by their paralogs (rows ## 31–33), as was the Hbb-b1-related pair (#10); it is supported by its paralogs in rows ## 11–14. Additionally, the second (minor) principal component, PC2 (Figure 3: Y-axis), is proportional to the difference between the estimates determined by the measurement in this work and corresponding estimates obtained independently by others. PC2 explains one-third (33%) of differential-gene-expression variance under study. It is parallel to the dashed line, where all single orthologs (Eif2b3, Ghitm, Mre11a, Orai1, Sh3bgr, Shox2, and Spint1) are located (Table 6: rows ## 6, 9, 28, 34, 51, and 52, respectively).
After that, keeping the two pairs of parallel lines in Figure 3 in mind (i.e., PC1 and the dotted line fitting orthologous DEGs with multiple homologs as well as PC2 and the dashed line fitting single orthologous DEGs), we performed a statistical analysis of the binary correlation between either multiple-homolog orthologous DEGs or single orthologous DEGs, namely, we tested whether there are the same or opposite signs of the log2 value within an orthologous DEG pair being tested (Figure 1: STEP 3).
To this end, we first formatted standard Fisher’s 2 × 2 table for binary correlation tests and then performed Fisher’s exact test, Pearson’s χ2 test, and binomial distribution analysis using the standard software, Statistica (StatsoftTM, Tulsa, OK, USA), as shown in Table 7. Within columns iii and iv of this table, readers can see that all the seven orthologous DEG pairs supported by multiple homologs have the same sign, in contrast to most of single orthologous DEG pairs (five of seven), which have opposite signs. This difference between single orthologous DEGs and those supported by multiple homologs is statistically significant according to Pearson’s χ2 test and Fisher’s exact test, as shown in columns vii and v of Table 7, respectively. Moreover, according to binomial distribution analysis (column v of Table 7), the orthologous DEG pairs supported by multiple homologs statistically significantly keep the sign of the log2 value in independent experiments on domestic versus wild animals (p < 10−3), whereas single orthologous DEGs are statistically insignificant in this regard (p > 0.2). Thus, the set of the DEGs identified by other authors in their experiments on domestic versus wild animals (Table 2) contains at least two biologically different subsets, namely: (a) PC1-linked orthologous DEGs supported by multiple homologs and (b) PC2-linked single orthologous DEGs.
Therefore, we finally performed a correlation analysis on the DEGs found in this work (in the hypothalamus of tame versus aggressive rats) with respect to both the PC1- and PC2-linked orthologous DEGs mentioned above (Figure 1: STEP 4) independently from one another, as readers can see in Figure 4a,b, respectively. As shown in Figure 4a, according to four statistical criteria—i.e., Pearson’s linear correlation (r), the Goodman–Kruskal generalized correlation (γ), and Spearman’s (R) and Kendall’s (τ) rank correlations—there are statistically significant positive correlations between the log2 values of DEGs in the hypothalamus of tame versus aggressive rats (X-axis; column iii of Table 6) [this work] and the log2 values of their orthologous DEGs (in domestic versus wild animals as measured by others: Y-axis; column ix of Table 6) supported by multiple homologs (i.e., PC1-linked).
Moreover, gray circles in this figure show similar pairs formed by DEGs identified here and their homologous DEGs identified by others and supported by multiple homologs (i.e., PC1-linked too). These circles fit reasonably well into the 95% confidence interval (between two dash-and-dot lines) of the linear correlation presented (a dotted line).
In Figure 4a, arrows point to the rat gene Pcdhb9 compared with its seven paralogs (rows ## 36–42 of Table 6), where two of these seven pairs fit within the 95% confidence interval (rows ## 36 and 38 in Table 6), while five remaining pairs fall outside this interval. Because the paralogous genes are homologs that arose via a duplication of their common ancestral gene and next via divergence in their biological functions, the observed partial deviation of these paralogous pairs from the common pattern of the genes-orthologs keeping functions from species to species looks expected rather than unexpected.
All these robust correlations taken together allow us for the first time to identify common orthologous DEGs of various domestic animals (versus their wild congeners) that from species to species and from tissue to tissue, statistically significantly keep the sign of the log2 value in line with principal component 1 of this domestication-related differential gene expression. PC1 explains two-thirds (67%) of this expression pattern variance (Figure 3 and Figure 4a). This finding may reflect the most common pattern of animal domestication.
Finally, Figure 4b presents significant negative correlations between the log2 values measured in this work (X-axis; column iii of Table 6) and those measured in experiments by others in domestic versus wild animals (Y-axis; column ix of Table 6) in the case of the PC2-linked single orthologous DEGs; these correlations are statistically significant according to Pearson’s linear correlation (r) and Spearman’s (R) and Kendall’s (τ) rank correlation analyses. In addition, gray circles depict similar single pairs of DEGs identified here with their homologous DEGs identified elsewhere (i.e., PC2-linked too) that fit reasonably well between two dash-and-dot lines, where the 95% confidence interval covers the linear correlation examined (a dotted line). Altogether, this robust evidence for the first time shows that some unique orthologous DEGs of various domestic versus wild animals can statistically significantly reverse the log2 value sign in agreement with the principal component (PC2) explaining one-third (33%) of this domestication-related differential-gene-expression variance (Figure 3 and Figure 4b). Indeed, this is exactly what we have already seen in both qPCR and the maximum-variation (maxVar) factor analysis [35], namely: two factors of the differential gene expression (in tame versus aggressive rats) that explain 67% of its variance: behavioral selection and laboratory lifestyle (i.e., domestication), while the remaining 33% of the variance can be explained by intraspecific variation [35]. Therefore, here the second principal component (PC2) explaining 33% of the differential gene expression variance between domestic and wild animals (Figure 3 and Figure 4b) reflects both interspecific variation and intraspecific variation and is close to the gold standard of variation in nature.
Thus, here we for the first time simultaneously quantified two independent phenomena—domestication [1] and cladogenesis [6]—at the same scale (genome-wide analysis). These phenomena respectively explained two-thirds and one-third of differential-gene-expression variance between domestic and wild animals as responses of their genomes to effects of artificial and natural environments. Nevertheless, it seems that the elucidation of the influence of both interspecific and intraspecific variation on the process of domestication of animals by humans requires further research, which is outside the scope of this work.

4. Discussion

Let us discuss biomedical applications of the results, keeping in mind that the mainstream in genome-wide studies on domestic versus wild animals is the search for species-specific DEGs [23,24,25,26,27,28,29]. There is an exception where investigators introduced the new biomedical concept of self-domestication syndrome [30], although the idea of human self-domestication is still debatable [31]. An algorithmic flowchart illustrating the biomedical application of our results to the search for human candidate genes contributing to self-domestication syndrome is presented in Figure 5. As readers can see in this figure, the test data under study included the 54 homologous DEG pairs of domestic versus wild animals (Table 6), where the total number of animal genes is 79.
Additionally, we took into account all the 68 human genes whose effects on human reproductive potential (as the most common index of how many chances humans have to survive, have children, and help them become the next generation under the best conditions [53,54]) have previously been estimated elsewhere [55,56] by means of SNPs within human gene promoters. In the present study, we updated these estimates in line with the current state of PubMed [52], as shown in Table S2.
First of all, using the above-mentioned test data, we compiled all possible (14) pairs of the human genes together with their homologous genes in animals, as depicted by a Venn diagram in Figure 5 (STEP 1). Table 8 presents these pairs of homologous human versus animal genes as follows: the left half of this table (i.e., columns i to v) is a copy of the data on a human gene in question from Table S2, whereas the right half (i.e., columns vi, vii, and x) is a copy of the RNA-Seq data on the corresponding homologous DEG in domestic versus wild animals (Table 6).
Furthermore, columns viii and ix of Table 8 translate a log2 value of a gene in an animal into either low or excessive expression of this gene during divergence of both domestic and wild forms of this animal from their most recent common ancestor. Recently [57,58], an RNA-Seq data analysis was based on this oldest phylogeny concept [59] widely used elsewhere [60,61,62,63]. Let us look at these orthologous genes one by one and discuss effects of their expression changes on reproductive potential in humans as well as during divergence of domestic and wild animals from their most recent com-mon ancestor [24,25,28,29,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86] (Table 8).
Human gene HBD produces hemoglobin subunit δ, a deficit of which (thalassemia) is clinically proven as a risk factor for auto-aggressive impulsiveness up to suicide [64], female subfertility [65], under-threshold IQ, and severe anxiety in children [66], as readers can see in Table 8. Actually, both suicide and IQ look like human-specific traits, which are biomedically studied mostly in human behavioral models based on animals [67,68] and are not common in the wild [56]. Curiously, anxiety is the most important trait for mutual trust within a human–pet pair, as independently discovered for dogs [69], sheep [70], and guinea pigs [71]. Moreover, according to a sports medicine report [72], combat success of healthy young boxers, kick boxers, and karate fighters increases with an increase in their anxiety in the arena (this anxiety prevents injuries until the end of a fight or sparring).
Table
Table 8. A comparison of the effects of changes in the expression of orthologous genes on human reproductive potential through aggressiveness changes and on various traits during the divergence of domestic animals and their wild congeners from the corresponding most recent common ancestor.
Table 8. A comparison of the effects of changes in the expression of orthologous genes on human reproductive potential through aggressiveness changes and on various traits during the divergence of domestic animals and their wild congeners from the corresponding most recent common ancestor.
HumansAnimals
GeneEffect of Gene Expression Changes on Human Reproductive Potential, Namely ($): Decreased (→) or Increased (←) RNA-SeqEffect of Gene Expression Changes during DIVERGENCE from the most Recent Common Ancestor[Ref]
Deficit (↓)$Excess (↑)$DEGlog2Deficit (↓)Excess (↑)
iiiiiiivvviviiviiiixx
HBDhemoglobin deficit (thalassemia) elevates risks of auto-aggressive impulsiveness up to suicide [64], female subfertility [65], causes under-threshold IQ and severe anxiety in children [66] →in cohort studies: elite athletes do high-altitude training rising hemoglobin level before low-altitude matches thereby increasing their chances of winning [76]←Hbb-b1−3.97tame rataggressive rat[75]
Hbbl−5.92dogswolves[24]
Hba1−4.06dogswolves[24]
Hbad−1.07domestic chickenwild chicken[29]
Hbm−6.46dogswolves[24]
Hbz1−7.10dogswolves[24]
NR5A1within human disease models based on Nr5a1-null male mice: hyper-anxiety during impaired aggressive sexual behavior up to male infertility in line with male patients carrying NR5A1 defects [77] as well as NR5A1 deficit can cause hypoestrogenism [78] leading to 1% of cases of female infertility [79]→in retrospective meta-analysis of PubMed content: NR5A1 excess contributes to excessive estrogen biosynthesis raising risks of estrogen-dependent inflammatory disorders in women [80] and vice versa for men [81]→Nr4a3−1,29tame rataggressive rat[82]
Nr4a3−0.85domestic chickenwild chicken[29]
Nr4a3−1.58domestic rabbitswild rabbits[28]
Nr2c1−0.74guinea pigscavy[25]
Nr3c10.51wild chickendomestic chicken[29]
Nr5a1−2.19guinea pigscavy[25]
SHOXin cohort studies: low SHOX expression causes short stature [83] as adaptive epigenetic response to adverse living conditions, when each calorie saved due to short stature helps to enhance stress resistance [84]←in cohort studies: girls carrying one extra SHOX copy have tall stature [85] elevating risks of pregnancy complications in military active-duty women [86]→Shox26.18aggressive rattame rat[87]
Shox2−3.43domestic rabbitswild rabbits[28]
Finally, via Stroop-like interference effect approximation [73], Nobel laureate Daniel Kahneman [74] highlighted anxiety among pivotal factors for human economic decision making during exposure to both psychological and social stressors. As shown in Table 8, a human hemoglobin deficit is consistent with that in tame rats [75], dogs [24], and domestic chickens [29] during their divergence from the most recent common ancestors along with their wild congeners (column viii) according to their negative log2 values (column vii).
As for human hemoglobin overexpression, according to a cohort-based study [76], elite athletes do high-altitude training raising the hemoglobin level before low-altitude matches thereby increasing their chances of winning. Thus, humans’ chances for success increase with a small subcritical increase in their hemoglobin level, consistently with a hemoglobin excess in aggressive rats [75], wolves [24], and wild chickens [29] during their microevolution (Table 8: column ix) according to their positive log2 values (Table 8: column vii). Altogether, readers can see in row #1 of Table 8 that animal genes that are homologs of the human HBD gene, from species to species are underexpressed in domestic animals and overexpressed in their wild congeners with respect to their most recent common ancestors.
This finding fits the first principal component (PC1) explaining two-thirds (67%) of the differential-gene-expression variance during the domestication-related microevolution (Figure 3 and Figure 4a). With this in mind, we suggest HBD as a candidate gene contributing to self-domestication syndrome, namely: low HBD expression might be regarded as what humans pay with health (e.g., higher risks of suicide [64], female subfertility [65], low IQ, and anxiety in children [66]) for the benefits received during evolution (e.g., the ability to build trust [69,70,71], prevention of injuries [72], and making adequate decisions under stress [74]).
Human gene NR5A1 encodes steroidogenic factor 1, both a deficit and excess of which reduce human reproductive potential on the one hand via hyper-anxiety during impaired aggressive sexual behavior up to male infertility [77] as well as hypoestrogenism [78] leading to 1% of cases of female infertility [79], and on the other hand, hyperestrogenism elevates risks of endometriosis and others estrogen-dependent inflammatory disorders in women [80,81]. That is why both domestic and wild forms of rats [82], chickens [29], rabbits [28], and guinea pigs [25] could have paid with their health (i.e., hormonal dysregulation) for the benefits that each of them got during microevolution (Table 8). Thus, we have no idea whether NR5A1 has something to do with self-domestication syndrome.
Human gene SHOX (short stature homeobox) underexpression is an adaptive epigenetic response to adverse living conditions in humans according to a cohort-based study [83] because each calorie saved due to short stature helps to enhance stress resistance [84]. This SHOX insufficiency in humans corresponds to a Shox2 deficit [87] in aggressive rats under study. Nonetheless, in another cohort-based study [85], researchers analyzed girls carrying one extra SHOX copy manifesting in tall stature, which is a risk factor for pregnancy complications in military active-duty women [86]. As readers can see in Table 4, Table 6, and Table 8, within the framework of our study, the human SHOX overexpression matches Shox2 overexpression [87] in tame rats. Let us recall Figure 3 and Figure 4b, where the animal Shox2 gene was seen within only a single orthologous pair of genes carrying opposite signs of log2 values compared with each other, in accordance with the second principal component (PC2) explaining one-third (33%) of the differential-gene-expression variance during domestication. Therefore, in contrast to tame rats, domestic rabbits seem to do well with short stature [83] because they could be anthropogenically artificially selected for the purpose of increasing their fertility for meat production, despite the tall-stature–related complications of pregnancy [85,86], as shown in the last row of Table 8. With this in mind, we would link the second principal component illustrated here by the animal Shox2 gene (Figure 3 and Figure 4b) to both interspecific and intraspecific variation.
Altogether, here we propose SHOX as a candidate human gene contributing to self-domestication syndrome as follows: SHOX excess as an epigenetic response to better life [84] resulting in tall stature [83] might be regarded as what humans pay with health (e.g., the tall-stature–related complications of pregnancy [85,86]) for the benefits received during evolution (e.g., the anthropogenic environment instead of the natural one).
Table 9 is standard Fisher’s 2 × 2 table that summarizes the findings of the comparative analysis of the above-mentioned similar genes of humans and animals (columns iii and iv) and presents the results of statistical analysis of these data, as shown in Figure 5 (STEP 2). As evident here, 13 and one of these domestic-animal DEGs correspond to human genes-markers of a decrease and increase in human reproductive potential, while the same is true for seven and seven DEGs in the wild animals.
Therefore, one can see that the DEGs in domestic animals statistically significantly match their human orthologous genes aggravating human diseases according to Pearson’s χ2 test (p < 0.05), Fisher’s exact test (p < 0.05), and binomial distribution analysis (p < 0.001). Finally, the last row of this table illustrates that DEGs of wild animals correspond to a set of human orthologous genes where some genes weaken while others improve human reproductive potential (p > 0.5, binomial distribution), overall corresponding to a norm (i.e., the wild type). Thus, the artificial environment of domestic animals alters the activity of their genes in the same direction as that seen in the corresponding human genes during some human diseases, whereas the natural environment maintains a normal gene expression pattern in wild animals (corresponding to health in humans).

5. Conclusions

First, in this study, we sequenced the hypothalamus transcriptome of tame and aggressive adult male rats and deposited these primary experimental data in the NCBI SRA database [40] (ID = PRJNA668014), where they are freely available for those who would like to use them in the future.
Second, in these data, we identified 46 DEGs (in the hypothalamus of the tame versus aggressive rats under study) that were statistically significant (PADJ < 0.05, according to correction for multiple comparisons). We selectively verified the reproducibility of these DEGs in another qPCR experiment on an independent set of biological samples.
Third, using principal component analysis, we for the first time compared the 46 hypothalamic DEGs of tame versus aggressive rats found here with 2347 DEGs of domestic versus wild animals found by others. This analysis yielded two principal components, PC1 and PC2, respectively explaining 67% and 33% of the differential-gene-expression variance between all the domestic and wild animals under study. In this way, we showed that PC1 corresponds mostly the orthologous DEGs supported by multiple homologs, which often kept the sign of their log2 values from species to species and from tissue to tissue as the common pattern of animal domestication. On the contrary, PC2 corresponds to the single orthologous DEGs without homologous supporting genes; these DEGs mainly reversed the sign of their log2 values from one species to another and from tissue to tissue; thus, PC2 may reflect both intraspecific and interspecific variation of gene expression alterations during domestication. This allows us to conclude that domestication explains two-thirds of differential-gene-expression variance between domestic and wild animals (i.e., PC1), whereas the remaining one-third reflects intraspecific and interspecific variation (i.e., PC2).

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ani11092667/s1, Table S1. Statistically significant correlations between the relative expression levels of the seven differentially expressed genes (DEGs) and one reference genes within the hypothalamus of tame versus aggressive rats, which were measured experimentally in vivo using RNA-Seq [this work] and qPCR [34] methods. Table S2. Effects of underexpression or overexpression of the human genes under this study on the human diseases through aggressiveness changes, as estimated [56,57].

Author Contributions

Conceptualization and supervision, N.A.K.; methodology, A.M.; investigation, I.C., R.K., N.K. and S.S.; software, A.B.; validation, L.S. and P.P.; resources, D.O.; data curation, V.S.; writing—original draft preparation, M.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Russian Science Foundation (grant No. 19-74-10041).

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki, Directive 2010/63/EU of the European Parliament and of the European Council of 22 September 2010. The research protocol was approved by the Interinstitutional Com-mission on Bioethics at the Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia (Approval documentation No. 8 dated 19 March 2012).

Informed Consent Statement

Not applicable.

Data Availability Statement

The primary RNA-Seq data obtained in this work were deposited in the NCBI SRA database (ID = PRJNA668014).

Acknowledgments

We are grateful to Nikolai Shevchuk (Shevchuk Editing Co., Brooklyn, NY, USA) for fruitful discussions of the work and assistance for scientific publications. We are also thankful to the Multi-Access Center “Bioinformatics” for the use of computational resources as supported by Russian government project #0259-2021-0009. We are appreciative of the Center for Genomic Research at the ICG SB RAS, where RNA-Seq was carried out.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Belyaev, D.K. Destabilizing selection as a factor in domestication. J. Hered. 1979, 70, 301–308. [Google Scholar] [CrossRef]
  2. Belyaev, D.K.; Evsikov, V.; Matysko, E.K. Genetics of animal fertility. 3. Effect of monohybrid heterosis on fertility and viability of minks, and prospects of its use in breeding. Sov. Genet. 1972, 8, 46–51. [Google Scholar]
  3. Belyaev, D.K.; Trut, L.N.; Ruvinsky, A.O. Genetics of the W Locus in Foxes and Expression of its Lethal Effects. J. Hered. 1975, 66, 331–338. [Google Scholar] [CrossRef]
  4. Belyaev, D.K.; Gruntenko, E.V. Strain Differences in Thymus Weight in Mice with Different Predispositions to Spontaneous Mammary Cancer. Nature 1972, 237, 401–402. [Google Scholar] [CrossRef] [PubMed]
  5. Osadchuk, A.V.; Markel’, A.L.; Khusainov, R.; Naumenko, E.V.; Beliaev, D.K. Problems in the genetics of stress. IV. A genetic analysis of the level of autonomic reactivity in emotional stress in rats. Genetika 1979, 15, 1847–1857. [Google Scholar] [PubMed]
  6. Ponomarenko, M.; Gunbin, K.; Doroshkov, A.; Kolchanov, N. Cladogenesis. In Brenner’s Encyclopedia of Genetics, 2nd ed.; Maloy, S., Hughes, K., Eds.; Academic Press: San Diego, CA, USA, 2013; Volume 2, pp. 21–24. [Google Scholar]
  7. Bray, E.E.; Otto, C.M.; Udell, M.A.R.; Hall, N.J.; Johnston, A.M.; MacLean, E.L. Enhancing the selection and performance of working dogs. Front. Vet. Sci. 2021, 8, 644431. [Google Scholar] [CrossRef]
  8. Eason, C.T.; Adams, S.L.; Puddick, J.; Romanazzi, D.; Miller, M.R.; King, N.; Johns, S.; Forbes-Blom, E.; Hessian, P.A.; Stamp, L.K.; et al. Greenshell™ Mussels: A Review of Veterinary Trials and Future Research Directions. Vet. Sci. 2018, 5, 36. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Peng, J.; Tang, Y.; Huang, Y. Gut health: The results of microbial and mucosal immune interactions in pigs. Anim. Nutr. 2021, 7, 282–294. [Google Scholar] [CrossRef]
  10. Ma, B.Y.; Gong, Q.L.; Sheng, C.Y.; Liu, Y.; Ge, G.Y.; Li, D.L.; Diao, N.C.; Shi, K.; Li, J.M.; Sun, Z.B.; et al. Prevalence of bovine leukemia in 1983-2019 in China: A systematic review and meta-analysis. Microb. Pathog. 2021, 150, 104681. [Google Scholar] [CrossRef]
  11. Orlando, L. The Evolutionary and Historical Foundation of the Modern Horse: Lessons from Ancient Genomics. Annu. Rev. Genet. 2020, 54, 563–581. [Google Scholar] [CrossRef]
  12. Thorne, J.W.; Murdoch, B.M.; Freking, B.A.; Redden, R.R.; Murphy, T.W.; Taylor, J.B.; Blackburn, H.D. Evolution of the sheep industry and genetic research in the United States: Opportunities for convergence in the twenty-first century. Anim. Genet. 2021, 52, 395–408. [Google Scholar] [CrossRef] [PubMed]
  13. Pathak, P.; Nayak, V.K.; Sinha, R.; Ganaie, B.A. Review on small ruminant conservation status and prospects in India. Trop. Anim. Health Prod. 2020, 52, 2817–2827. [Google Scholar] [CrossRef] [PubMed]
  14. Rajkumar, U.; Rao, S.V.R.; Raju, M.V.L.N.; Chatterjee, R.N. Backyard poultry farming for sustained production and enhanced nutritional and livelihood security with special reference to India: A review. Trop. Anim. Health Prod. 2021, 53, 1–13. [Google Scholar] [CrossRef] [PubMed]
  15. Wei, R.; Han, C.; Deng, D.; Ye, F.; Gan, X.; Liu, H.; Li, L.; Xu, H.; Wei, S. Research progress into the physiological changes in metabolic pathways in waterfowl with hepatic steatosis. Br. Poult. Sci. 2020, 62, 118–124. [Google Scholar] [CrossRef]
  16. Gao, G.; Gao, D.; Zhao, X.; Xu, S.; Zhang, K.; Wu, R.; Yin, C.; Li, J.; Xie, Y.; Hu, S.; et al. Genome-Wide Association Study-Based Identification of SNPs and Haplotypes Associated with Goose Reproductive Performance and Egg Quality. Front. Genet. 2021, 12, 602583. [Google Scholar] [CrossRef]
  17. Courtier-Orgogozo, V.; Martin, A. The coding loci of evolution and domestication: Current knowledge and implications for bio-inspired genome editing. J. Exp. Biol. 2020, 223, jeb208934. [Google Scholar] [CrossRef] [Green Version]
  18. Cowan, M.A.; Callan, M.N.; Watson, M.J.; Watson, D.M.; Doherty, T.S.; Michael, D.R.; Dunlop, J.A.; Turner, J.M.; Moore, H.A.; Watchorn, D.J.; et al. Artificial refuges for wildlife conservation: What is the state of the science? Biol. Rev. 2021. [Google Scholar] [CrossRef]
  19. Belyaev, D.K.; Borodin, P.M. The influence of stress on variation and its role in evolution. Biol. Zent. 1982, 100, 705–714. [Google Scholar]
  20. Plyusnina, I.; Oskina, I. Behavioral and Adrenocortical Responses to Open-Field Test in Rats Selected for Reduced Aggressiveness Toward Humans. Physiol. Behav. 1997, 61, 381–385. [Google Scholar] [CrossRef]
  21. McGonigle, P.; Ruggeri, B. Animal models of human disease: Challenges in enabling translation. Biochem. Pharmacol. 2014, 87, 162–171. [Google Scholar] [CrossRef]
  22. Lutz, C.; Maher, L.; Lee, C.; Kang, W. COVID-19 preclinical models: Human angiotensin-converting enzyme 2 transgenic mice. Hum. Genom. 2020, 14, 1–11. [Google Scholar] [CrossRef]
  23. Hekman, J.P.; Johnson, J.; Edwards, W.; Vladimirova, A.V.; Gulevich, R.G.; Ford, A.L.; Kharlamova, A.V.; Herbeck, Y.; Acland, G.M.; Raetzman, L.T.; et al. Anterior Pituitary Transcriptome Suggests Differences in ACTH Release in Tame and Aggressive Foxes. G3 Genes Genomes Genet. 2018, 8, 859–873. [Google Scholar] [CrossRef] [Green Version]
  24. Yang, X.; Zhang, H.; Shang, J.; Liu, G.; Xia, T.; Zhao, C.; Sun, G.; Dou, H. Comparative analysis of the blood transcriptomes between wolves and dogs. Anim. Genet. 2018, 49, 291–302. [Google Scholar] [CrossRef]
  25. Albert, F.W.; Somel, M.; Carneiro, M.; Aximu-Petri, A.; Halbwax, M.; Thalmann, O.; Blanco-Aguiar, J.; Plyusnina, I.Z.; Trut, L.; Villafuerte, R.; et al. A Comparison of Brain Gene Expression Levels in Domesticated and Wild Animals. PLoS Genet. 2012, 8, e1002962. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Long, K.; Mao, K.; Che, T.; Zhang, J.; Qiu, W.; Wang, Y.; Tang, Q.; Ma, J.; Li, M.; Li, X. Transcriptome differences in frontal cortex between wild boar and domesticated pig. Anim. Sci. J. 2018, 89, 848–857. [Google Scholar] [CrossRef]
  27. Yang, Y.; Adeola, A.C.; Xie, H.B.; Zhang, Y.P. Genomic and transcriptomic analyses reveal selection of genes for puberty in Bama Xiang pigs. Zool. Res. 2018, 39, 424–430. [Google Scholar] [CrossRef]
  28. Sato, D.X.; Rafati, N.; Ring, H.; Younis, S.; Feng, C.; Blanco-Aguiar, J.A.; Rubin, C.-J.; Villafuerte, R.; Hallböök, F.; Carneiro, M.; et al. Brain Transcriptomics of Wild and Domestic Rabbits Suggests That Changes in Dopamine Signaling and Ciliary Function Contributed to Evolution of Tameness. Genome Biol. Evol. 2020, 12, 1918–1928. [Google Scholar] [CrossRef] [PubMed]
  29. Fallahshahroudi, A.; Lotvedt, P.; Belteky, J.; Altimiras, J.; Jensen, P. Changes in pituitary gene expression may underlie multiple domesticated traits in chickens. Heredity 2019, 122, 195–204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Theofanopoulou, C.; Gastaldon, S.; O’Rourke, T.; Samuels, B.D.; Messner, A.; Martins, P.T.; Delogu, F.; Alamri, S.; Boeckx, C. Self-domestication in Homo sapiens: Insights from comparative genomics. PLoS ONE 2017, 12, e0185306. [Google Scholar] [CrossRef] [Green Version]
  31. Del Savio, L.; Mameli, M. Human domestication and the roles of human agency in human evolution. Hist. Philos. Life Sci. 2020, 42, 21. [Google Scholar] [CrossRef]
  32. Gunbin, K.; Ponomarenko, M.P.; Suslov, V.V.; Gusev, F.; Fedonin, G.G.; Rogaev, E.I. Evolution of Brain Active Gene Promoters in Human Lineage Towards the Increased Plasticity of Gene Regulation. Mol. Neurobiol. 2017, 55, 1871–1904. [Google Scholar] [CrossRef]
  33. Chadaeva, I.; Ponomarenko, P.; Rasskazov, D.; Sharypova, E.; Kashina, E.; Kleshchev, M.; Ponomarenko, M.; Naumenko, V.; Savinkova, L.; Kolchanov, N.; et al. Natural Selection Equally Supports the Human Tendencies in Subordination and Domination: A Genome-Wide Study With in silico Confirmation and in vivo Validation in Mice. Front. Genet. 2019, 10, 73. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Oshchepkov, D.; Ponomarenko, M.; Klimova, N.; Chadaeva, I.; Bragin, A.; Sharypova, E.; Shikhevich, S.; Kozhemyakina, R. A Rat Model of Human Behavior Provides Evidence of Natural Selection against Underexpression of Aggressiveness-Related Genes in Humans. Front. Genet. 2019, 10, 1267. [Google Scholar] [CrossRef] [Green Version]
  35. Tenenhaus, M.; Tenenhaus, A.; Groenen, P.J.F. Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods. Psychometrika 2017, 82, 737–777. [Google Scholar] [CrossRef]
  36. Klimova, N.V.; Chadaeva, I.V.; Shichevich, S.G.; Kozhemyakina, R.V. Differential expression of 10 genes in the hypothalamus of two generations of rats selected for a reaction to humans. Vavilovskii Zhurnal Genet. Sel. 2021, 25, 208–215. [Google Scholar]
  37. Naumenko, E.; Popova, N.; Nikulina, E.; Dygalo, N.; Shishkina, G.; Borodin, P.; Markel, A. Behavior, adrenocortical activity, and brain monoamines in Norway rats selected for reduced aggressiveness towards man. Pharmacol. Biochem. Behav. 1989, 33, 85–91. [Google Scholar] [CrossRef]
  38. Paxinos, G.; Watson, C.R. The Rat Brain in Stereotaxic Coordinates, 7th ed.; Academic Press: London, UK, 2013; p. 472. [Google Scholar]
  39. Walker, S.E.; Papilloud, A.; Huzard, D.; Sandi, C. The link between aberrant hypothalamic–pituitary–adrenal axis activity during development and the emergence of aggression—Animal studies. Neurosci. Biobehav. Rev. 2018, 91, 138–152. [Google Scholar] [CrossRef] [PubMed]
  40. Sayers, E.W.; Beck, J.; Bolton, E.; Bourexis, D.; Brister, J.R.; Canese, K.; Comeau, D.C.; Funk, K.; Kim, S.; Klimke, W.; et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2020, 49, D10–D17. [Google Scholar] [CrossRef]
  41. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [Green Version]
  42. Kim, D.; Pertea, G.; Trapnell, C.; Pimentel, H.; Kelley, R.; Salzberg, S.L. TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013, 14, 36. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R. The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics 2009, 25, 2078–2079. [Google Scholar] [CrossRef] [Green Version]
  44. Anders, S.; Pyl, P.T.; Huber, W. HTSeq—A Python framework to work with high-throughput sequencing data. Bioinformatics 2015, 31, 166–169. [Google Scholar] [CrossRef]
  45. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Anders, S.; Huber, W. Differential expression analysis for sequence count data. Genome Biol. 2010, 11, 106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Ye, J.; Coulouris, G.; Zaretskaya, I.; Cutcutache, I.; Rozen, S.; Madden, T.L. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 2012, 13, 134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Tian, L.; Chen, Y.; Wang, D.W.; Liu, X.H. Validation of reference genes via qRT-PCR in multiple conditions in brandt’s voles, lasiopodomys brandtii. Animals 2021, 11, 897. [Google Scholar] [CrossRef] [PubMed]
  49. Zamani, A.; Powell, K.L.; May, A.; Semple, B.D. Validation of reference genes for gene expression analysis following experimental traumatic brain injury in a pediatric mouse model. Brain Res. Bull. 2020, 156, 43–49. [Google Scholar] [CrossRef]
  50. Gholami, K.; Loh, S.Y.; Salleh, N.; Lam, S.K.; Hoe, S.Z. Selection of suitable endogenous reference genes for qPCR in kidney and hypothalamus of rats under testosterone influence. PLoS ONE 2017, 12, e0176368. [Google Scholar] [CrossRef] [Green Version]
  51. Penning, L.C.; Vrieling, H.E.; Brinkhof, B.; Riemers, F.M.; Rothuizen, J.; Rutteman, G.R.; Hazewinkel, H.A. A validation of 10 feline reference genes for gene expression measurements in snap-frozen tissues. Vet. Immunol. Immunopathol. 2007, 120, 212–222. [Google Scholar] [CrossRef]
  52. Lu, Z. PubMed and beyond: A survey of web tools for searching biomedical literature. Database 2011, 2011, baq036. [Google Scholar] [CrossRef] [Green Version]
  53. Chapman, R.N. Animal ecology, with special reference to insects. J. Nerv. Ment. Dis. 1933, 78, 680. [Google Scholar] [CrossRef]
  54. Pianka, E.R. Natural Selection of Optimal Reproductive Tactics. Am. Zool. 1976, 16, 775–784. [Google Scholar] [CrossRef]
  55. Chadaeva, I.; Ponomarenko, P.M.; Rasskazov, D.A.; Sharypova, E.B.; Kashina, E.V.; Zhechev, D.A.; Drachkova, I.A.; Arkova, O.V.; Savinkova, L.K.; Ponomarenko, M.P.; et al. Candidate SNP markers of reproductive potential are predicted by a significant change in the affinity of TATA-binding protein for human gene promoters. BMC Genom. 2018, 19. [Google Scholar] [CrossRef] [Green Version]
  56. Ponomarenko, M.; Kleshchev, M.; Ponomarenko, P.; Chadaeva, I.; Sharypova, E.; Rasskazov, D.; Kolmykov, S.; Drachkova, I.; Vasiliev, G.; Gutorova, N.; et al. Disruptive natural selection by male reproductive potential prevents underexpression of protein-coding genes on the human Y chromosome as a self-domestication syndrome. BMC Genet. 2020, 21, 89. [Google Scholar] [CrossRef] [PubMed]
  57. Vasiliev, G.; Chadaeva, I.; Rasskazov, D.; Ponomarenko, P.; Sharypova, E.; Drachkova, I.; Bogomolov, A.; Savinkova, L.; Ponomarenko, M.; Kolchanov, N.; et al. A Bioinformatics Model of Human Diseases on the Basis of Differentially Expressed Genes (of Domestic Versus Wild Animals) That Are Orthologs of Human Genes Associated with Reproductive-Potential Changes. Int. J. Mol. Sci. 2021, 22, 2346. [Google Scholar] [CrossRef]
  58. Klimova, N.V.; Oshchepkova, E.; Chadaeva, I.; Sharypova, E.; Ponomarenko, P.; Drachkova, I.; Rasskazov, D.; Oshchepkov, D.; Ponomarenko, M.; Savinkova, L.; et al. Disruptive Selection of Human Immunostimulatory and Immunosuppressive Genes Both Provokes and Prevents Rheumatoid Arthritis, Respectively, as a Self-Domestication Syndrome. Front. Genet. 2021, 12, 610774. [Google Scholar] [CrossRef] [PubMed]
  59. Samet, H. A top-down quadtree traversal algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 1985, 7, 94–98. [Google Scholar] [CrossRef]
  60. Sun, G.-L.; Shen, W.; Wen, J.-F. Triosephosphate Isomerase Genes in Two Trophic Modes of Euglenoids (Euglenophyceae) and Their Phylogenetic Analysis. J. Eukaryot. Microbiol. 2008, 55, 170–177. [Google Scholar] [CrossRef]
  61. Morozova, O.V.; Alekseeva, A.E.; Sashina, T.A.; Brusnigina, N.F.; Epifanova, N.V.; Kashnikov, A.U.; Zverev, V.V.; Novikova, N.A. Phylodynamics of G4P[8] and G2P[4] strains of rotavirus A isolated in Russia in 2017 based on full-genome analyses. Virus Genes 2020, 56, 537–545. [Google Scholar] [CrossRef] [PubMed]
  62. Hakizimana, J.N.; Yona, C.; Kamana, O.; Nauwynck, H.; Misinzo, G. African Swine Fever Virus Circulation between Tanzania and Neighboring Countries: A Systematic Review and Meta-Analysis. Viruses 2021, 13, 306. [Google Scholar] [CrossRef] [PubMed]
  63. Zhang, Y.; Katoh, T.K.; Finet, C.; Izumitani, H.F.; Toda, M.J.; Watabe, H.-A.; Katoh, T. Phylogeny and evolution of mycophagy in the Zygothrica genus group (Diptera: Drosophilidae). Mol. Phylogenet. Evol. 2021, 163, 107257. [Google Scholar] [CrossRef] [PubMed]
  64. Namazi, M.R. Minor thalassemia may be a risk factor for impulsiveness. Med. Hypotheses 2003, 60, 335–336. [Google Scholar] [CrossRef]
  65. Takhviji, V.; Zibara, K.; Azarkeivan, A.; Mehrvar, N.; Mehrvar, N.; Mezginejad, F.; Khosravi, A. Fertility and pregnancy in Iranian thalassemia patients: An update on transfusion complications. Transfus. Med. 2020, 30, 352–360. [Google Scholar] [CrossRef] [PubMed]
  66. Elalfy, M.S.; Ibrahim, A.S.; Ibrahim, G.S.; Hussein, H.M.A.G.; Mohammed, H.G.E.; Ebeid, F.S.E. Hidden brain iron content in sickle cell disease: Impact on neurocognitive functions. Eur. J. Nucl. Med. Mol. Imaging 2021, 180, 2677–2686. [Google Scholar] [CrossRef]
  67. Moffitt, T.; Brammer, G.L.; Caspi, A.; Fawcett, J.; Raleigh, M.; Yuwiler, A.; Silva, P. Whole Blood Serotonin Relates to Violence in an Epidemiological Study. Biol. Psychiatry 1998, 43, 446–457. [Google Scholar] [CrossRef]
  68. Mørkholt, A.S.; Wiborg, O.; Nieland, J.G.K.; Nielsen, S.; Nieland, J.D. Blocking of carnitine palmitoyl transferase 1 potently reduces stress-induced depression in rat highlighting a pivotal role of lipid metabolism. Sci. Rep. 2017, 7, 2158. [Google Scholar] [CrossRef] [Green Version]
  69. Zapata, I.; Serpell, J.A.; Alvarez, C.E. Genetic mapping of canine fear and aggression. BMC Genom. 2016, 17, 572. [Google Scholar] [CrossRef] [Green Version]
  70. Coulon, M.; Lévy, F.; Ravel, C.; Nowak, R.; Boissy, A. Mild effects of gestational stress and social reactivity on the onset of mother–young interactions and bonding in sheep. Stress 2014, 17, 460–470. [Google Scholar] [CrossRef] [PubMed]
  71. Kaiser, S.; Hennessy, M.; Sachser, N. Domestication affects the structure, development and stability of biobehavioural profiles. Front. Zool. 2015, 12, S19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Tiric-Campara, M.; Tupkovic, E.; Mazalovic, E.; Karalic, E.; Biscevic, M.; Djelilovic-Vranic, J.; Alajbegovic, A. Correlation of aggressiveness and anxiety in fighting sports. Med. Arch. 2012, 66, 116–121. [Google Scholar] [CrossRef] [Green Version]
  73. Stroop, J.R. Studies of interference in serial verbal reactions. J. Exp. Psychol. 1935, 18, 643–662. [Google Scholar] [CrossRef]
  74. Keinan, G.; Friedland, N.; Kahneman, D.; Roth, D. The effect of stress on the suppression of erroneous competing responses. Anxiety Stress. Coping 1999, 12, 455–476. [Google Scholar] [CrossRef]
  75. Smith, J.R.; Hayman, G.T.; Wang, S.-J.; Laulederkind, S.J.F.; Hoffman, M.J.; Kaldunski, M.L.; Tutaj, M.; Thota, J.; Nalabolu, H.S.; Ellanki, S.L.R.; et al. The Year of the Rat: The Rat Genome Database at 20: A multi-species knowledgebase and analysis platform. Nucleic Acids Res. 2019, 48, D731–D742. [Google Scholar] [CrossRef] [PubMed]
  76. Hauser, A.; Troesch, S.; Steiner, T.; Brocherie, F.; Girard, O.; Saugy, J.J.; Schmitt, L.; Millet, G.P.; Wehrlin, J.P. Do male athletes with already high initial haemoglobin mass benefit from ‘live high-train low’ altitude training? Exp. Physiol. 2018, 103, 68–76. [Google Scholar] [CrossRef]
  77. Büdefeld, T.; Tobet, S.A.; Majdic, G. Steroidogenic Factor 1 and the Central Nervous System. J. Neuroendocrinol. 2011, 24, 225–235. [Google Scholar] [CrossRef] [PubMed]
  78. Domenice, S.; Machado, A.Z.; Ferreira, F.M.; Ferraz-de-Souza, B.; Lerario, A.M.; Lin, L.; Nishi, M.Y.; Gomes, N.L.; da Silva, T.E.; Silva, R.B.; et al. Wide spectrum of NR5A1-related phenotypes in 46,XY and 46,XX individuals. Birth Defects Res. Part C Embryo Today 2016, 108, 309–320. [Google Scholar]
  79. Fu, Y.-X.; Ji, J.; Shan, F.; Li, J.; Hu, R. Human mesenchymal stem cell treatment of premature ovarian failure: New challenges and opportunities. Stem Cell Res. Ther. 2021, 12, 161. [Google Scholar] [CrossRef] [PubMed]
  80. Yilmaz, B.D.; Bulun, S. Endometriosis and nuclear receptors. Hum. Reprod. Updat. 2019, 25, 473–485. [Google Scholar] [CrossRef] [PubMed]
  81. Bösch, F.; Angele, M.K.; Chaudry, I.H. Gender differences in trauma, shock and sepsis. Mil. Med. Res. 2018, 5, 35. [Google Scholar] [CrossRef]
  82. Kumar, R.; Thompson, E. The structure of the nuclear hormone receptors. Steroids 1999, 64, 310–319. [Google Scholar] [CrossRef]
  83. Hoffmann, S.; Roeth, R.; Diebold, S.; Gogel, J.; Hassel, D.; Just, S.; Rappold, G.A. Identification and Tissue-Specific Characterization of Novel SHOX-Regulated Genes in Zebrafish Highlights SOX Family Members Among Other Genes. Front. Genet. 2021, 12, 688808. [Google Scholar] [CrossRef] [PubMed]
  84. German, A.; Mesch, G.; Hochberg, Z. People Are Taller in Countries with Better Environmental Conditions. Front. Endocrinol. 2020, 11, 106. [Google Scholar] [CrossRef] [Green Version]
  85. Upners, E.N.; Jensen, R.B.; Meyts, E.R.; Dunø, M.; Aksglaede, L.; Juul, A. Short stature homeobox-containing gene duplications in 3.7% of girls with tall stature and normal karyotypes. Acta Paediatr. 2017, 106, 1651–1657. [Google Scholar] [CrossRef] [PubMed]
  86. Magann, E.F.; Winchester, M.I.; Carter, D.P.; Martin, J.N.; Bass, J.D.; Morrison, J.C. Factors Adversely Affecting Pregnancy Outcome in the Military. Am. J. Perinatol. 1995, 12, 462–466. [Google Scholar] [CrossRef] [PubMed]
  87. Rosin, J.M.; Abassah-Oppong, S.; Cobb, J. Comparative transgenic analysis of enhancers from the human SHOX and mouse Shox2 genomic regions. Hum. Mol. Genet. 2013, 22, 3063–3076. [Google Scholar] [CrossRef] [PubMed] [Green Version]
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Figure 1. An algorithmic flowchart illustrating the verification of the DEGs found in this study in the hypothalamus of tame versus aggressive rats with respect to their known homologous DEGs in domestic animals compared with their wild congeners, as independently reported by others. Legend: log2, the log2-transformed fold change (i.e., the ratio of a domestic-animal gene expression level to that in wild animals); PC1 and PC2: first and second principal components, respectively.
Figure 1. An algorithmic flowchart illustrating the verification of the DEGs found in this study in the hypothalamus of tame versus aggressive rats with respect to their known homologous DEGs in domestic animals compared with their wild congeners, as independently reported by others. Legend: log2, the log2-transformed fold change (i.e., the ratio of a domestic-animal gene expression level to that in wild animals); PC1 and PC2: first and second principal components, respectively.
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Animals 11 02667 g002 550
Figure 2. Selective qPCR-based verification of the DEGs identified in this work in the hypothalamus of tame versus aggressive rats. (a) In tame rats (white bars) versus aggressive rats (grey bars), all the three DEGs examined (i.e., Ascl3, Apobec1, and Defb17) are statistically significantly overexpressed in the hypothalamus (here, bar height: mean; error bars: standard error of mean [SEM]; and asterisks (i.e., the characters "*") denote statistical significance at p < 0.05 according to both the Mann–Whitney U test and Fisher’s Z-test). (b) Statistically significant correlations between the log2 value of the three selected DEGs and three reference genes [i.e., B2m, Ppia, and Rpl30] in the hypothalamus of tame versus aggressive rats (open circles), as measured experimentally by RNA-Seq (X-axis) and qPCR (Y-axis). Solid and dash-and-dot lines denote linear regression and boundaries of its 95% confidence interval calculated by means of software package Statistica (StatsoftTM, Tulsa, OK, USA). Statistics: r, R, τ, γ, and p are coefficients of Pearson’s linear correlation, Spearman’s rank correlation, Kendall’s rank correlation, Goodman–Kruskal generalized correlation, and their p values (statistical significance), respectively. Filled circles depict the statistically significant DEGs in the hypothalamus of tame vs. aggressive rats according to the independent qPCR-based identification published elsewhere [34]. These qPCR [34] and RNA-Seq [this work] data are given in Table S1, where their Pearson’s linear correlation is statistically significant, r = 0.71 at p < 0.05 (hereinafter, see Supplementary Materials).
Figure 2. Selective qPCR-based verification of the DEGs identified in this work in the hypothalamus of tame versus aggressive rats. (a) In tame rats (white bars) versus aggressive rats (grey bars), all the three DEGs examined (i.e., Ascl3, Apobec1, and Defb17) are statistically significantly overexpressed in the hypothalamus (here, bar height: mean; error bars: standard error of mean [SEM]; and asterisks (i.e., the characters "*") denote statistical significance at p < 0.05 according to both the Mann–Whitney U test and Fisher’s Z-test). (b) Statistically significant correlations between the log2 value of the three selected DEGs and three reference genes [i.e., B2m, Ppia, and Rpl30] in the hypothalamus of tame versus aggressive rats (open circles), as measured experimentally by RNA-Seq (X-axis) and qPCR (Y-axis). Solid and dash-and-dot lines denote linear regression and boundaries of its 95% confidence interval calculated by means of software package Statistica (StatsoftTM, Tulsa, OK, USA). Statistics: r, R, τ, γ, and p are coefficients of Pearson’s linear correlation, Spearman’s rank correlation, Kendall’s rank correlation, Goodman–Kruskal generalized correlation, and their p values (statistical significance), respectively. Filled circles depict the statistically significant DEGs in the hypothalamus of tame vs. aggressive rats according to the independent qPCR-based identification published elsewhere [34]. These qPCR [34] and RNA-Seq [this work] data are given in Table S1, where their Pearson’s linear correlation is statistically significant, r = 0.71 at p < 0.05 (hereinafter, see Supplementary Materials).
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Figure 3. The results of principal component analysis of the hypothalamic DEGs of tame versus aggressive rats found in this work in comparison with their known homologous DEGs in domestic animals versus their wild congeners as reported by others. Legend: see the footnote of Table 4 and legend of Figure 1; open circles: all seven single orthologs Eif2b3 (Table 6: row #6), Ghitm (#9), Mre11a (#28), Orai1 (#34), Sh3bgr (#51), Shox2 (#52), and Spint1 (#53) grouped along a dashed line; filled circles: all seven orthologs supported by other homologs Banp (#1 and #2, which support one another in homology), Cd22 (#3 and #4 supporting each other in homology), Nr4a3 (#29 and #30 supported one another in homology as well as by their paralogs in rows ## 31–33), and Hbb-b1 (#10 supported by its paralogs in rows ## 11–14) grouped along a dotted line; grey circles: all the remaining homologs; log2 (this work) and log2 (other works) correspond to columns iii and ix of Table 6; PC1 and PC2: first (major) and second (minor) principal components calculated by means of software package Statistica (StatsoftTM, Tulsa, OK, USA); they are parallel to the dotted and dashed lines, respectively.
Figure 3. The results of principal component analysis of the hypothalamic DEGs of tame versus aggressive rats found in this work in comparison with their known homologous DEGs in domestic animals versus their wild congeners as reported by others. Legend: see the footnote of Table 4 and legend of Figure 1; open circles: all seven single orthologs Eif2b3 (Table 6: row #6), Ghitm (#9), Mre11a (#28), Orai1 (#34), Sh3bgr (#51), Shox2 (#52), and Spint1 (#53) grouped along a dashed line; filled circles: all seven orthologs supported by other homologs Banp (#1 and #2, which support one another in homology), Cd22 (#3 and #4 supporting each other in homology), Nr4a3 (#29 and #30 supported one another in homology as well as by their paralogs in rows ## 31–33), and Hbb-b1 (#10 supported by its paralogs in rows ## 11–14) grouped along a dotted line; grey circles: all the remaining homologs; log2 (this work) and log2 (other works) correspond to columns iii and ix of Table 6; PC1 and PC2: first (major) and second (minor) principal components calculated by means of software package Statistica (StatsoftTM, Tulsa, OK, USA); they are parallel to the dotted and dashed lines, respectively.
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Figure 4. Statistically significant correlations between the log2 values of DEGs in the hypothalamus of tame vs. aggressive rats as measured in this work (X-axis; column iii of Table 6) and the log2 values of their known orthologous DEGs in domestic vs. wild animals as measured by others (Y-axis; column ix of Table 6). Legend: see legends of Figure 2 and Figure 3; (a) PC1-linked orthologous DEGs supported by multiple homologs; arrows (→) point to Pcdhb9 compared with its seven paralogs (rows ## 36–42 of Table 6); (b) PC2-linked single orthologous DEGs.
Figure 4. Statistically significant correlations between the log2 values of DEGs in the hypothalamus of tame vs. aggressive rats as measured in this work (X-axis; column iii of Table 6) and the log2 values of their known orthologous DEGs in domestic vs. wild animals as measured by others (Y-axis; column ix of Table 6). Legend: see legends of Figure 2 and Figure 3; (a) PC1-linked orthologous DEGs supported by multiple homologs; arrows (→) point to Pcdhb9 compared with its seven paralogs (rows ## 36–42 of Table 6); (b) PC2-linked single orthologous DEGs.
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Figure 5. An algorithmic flowchart depicting biomedical application of our results to the search for human candidate genes contributing to self-domestication syndrome. Legend: see the footnotes of Table 4 and Table 6 as well as legends of Figure 1, Figure 2 and Figure 3.
Figure 5. An algorithmic flowchart depicting biomedical application of our results to the search for human candidate genes contributing to self-domestication syndrome. Legend: see the footnotes of Table 4 and Table 6 as well as legends of Figure 1, Figure 2 and Figure 3.
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Table
Table 1. For quantitative polymerase chain reaction (qPCR), primers were selected using Web service PrimerBLAST [47].
Table 1. For quantitative polymerase chain reaction (qPCR), primers were selected using Web service PrimerBLAST [47].
No.Rat GeneNCBI Gene IDDirect, 5′→3′Reverse, 5′→3′
iiiiiiivv
DEGs Identifiedin Hypothalamus of Tame versus Aggressive Rats [This Work]
1Apobec125383CGCCGCAACATAAGCTCCCGATGCTGTGCCTTCCTCCCCAGTTG
2Ascl3246301CCTCTGCTGCCCTTTTCCAGACTTGACTCGCTGCCTCTCT
3Defb17641658TGGTAGCTTGGACTTGAGGAAAGAATGCAGCAGTGTGTTCCAGGTC
Reference Genes
4B2m24223GTGTCTCAGTTCCACCCACCTTACATGTCTCGGTCCCAGG
5Hprt124465TCCCAGCGTCGTGATTAGTGACCTTCATGACATCTCGAGCAAG
6Ppia25518TTCCAGGATTCATGTGCCAGCTTGCCATCCAGCCACTC
7Rpl3064640CATCTTGGCGTCTGATCTTGTCAGAGTCTGTTTGTACCCC
Notes. The rat differentially expressed genes (DEGs) subjected to this qPCR verification: Apobec1, apolipoprotein B mRNA editing enzyme catalytic subunit 1; Ascl3, achaete-scute family bHLH transcription factor 3; Defb17, defensin β 17; B2m, β-2-microglobulin [48]; Hprt1, hypoxanthine phosphoribosyltransferase 1 [49]; Ppia, peptidylprolyl isomerase A [50]; and Rpl30, ribosomal protein L30 [51].
Table
Table 2. The investigated genome-wide transcriptomes of domestic versus wild animals, all the data that we could find in PubMed [52].
Table 2. The investigated genome-wide transcriptomes of domestic versus wild animals, all the data that we could find in PubMed [52].
#Wild AnimalsDomestic AnimalsTissueNDEG[Ref]
1aggressive foxes (Vulpes vulpes)tame foxes (V. vulpes) pituitary327[23]
2wolves (Canis familiaris)dogs (C. lupus)blood450[24]
3wolves (C. lupus)dogs (C. familiaris) frontal cortex13[25]
4Boars (Sus scrofa) pigs (S. scrofa)frontal cortex30[25]
5cavy
(Cavia aperea)		guinea pigs
(C. porcellus)		frontal cortex883[25]
6wild rabbits (Oryctolagus cuniculus)domestic rabbits (O. cuniculus domesticus)frontal cortex17[25]
7aggressive rats (R. norvegicus)tame rats (Rattus norvegicus)frontal cortex20[25]
8boars (S. scrofa)pigs (S. scrofa)frontal cortex34[26]
9boars (S. scrofa)pigs (S. scrofa)pituitary22[27]
10wild rabbits (O. cuniculus)domestic rabbits (O. cuniculus domesticus)parietal-temporal cortex216[28]
11wild rabbits (O. cuniculus)domestic rabbits (O. cuniculus domesticus)amygdala118[28]
12wild rabbits (O. cuniculus)domestic rabbits (O. cuniculus domesticus)hypothalamus43[28]
13wild rabbits (O. cuniculus)domestic rabbits (O. cuniculus domesticus)hippocampus100[28]
14wild chicken (G. gallus)domestic chicken (Gallus gallus)pituitary474[29]
Σ7 wild animal species: 7 domestic animal species: 7 tissues23477 Refs
Note: NDEG, number of DEGs; Ref, reference.
Table
Table 3. A summary of transcriptome sequencing in the hypothalamus of three tame rats and three aggressive ones (all unrelated).
Table 3. A summary of transcriptome sequencing in the hypothalamus of three tame rats and three aggressive ones (all unrelated).
GroupTame vs. Aggressive Rats
Total sequence reads (NCBI SRA, ID = PRJNA668014)219,086,104
Reads mapped to reference rat genome RGSC Rnor_6.0, UCSC Rn6, July 2014 (%)184,991,379 (84.44%)
Expressed genes identified14,039
Candidate DEGs identified (p value < 0.05; Fisher’s Z-test)1025
Statistically significant DEGs (PADJ < 0.05, Benjamini correction)46
Table
Table 4. The statistically significant DEGs in the hypothalamus of tame versus aggressive rats bred artificially during 90 generations from the same common ancestral subpopulation.
Table 4. The statistically significant DEGs in the hypothalamus of tame versus aggressive rats bred artificially during 90 generations from the same common ancestral subpopulation.
Rat GeneDifferential Expression
No.SymbolNamelog2pPADJ
1Ascl3achaete-scute family bHLH transcription factor 33.9910−1210−8
2Morn1MORN repeat containing 11.2410−1010−6
3Krt2keratin 2−1.6510−810−4
4BanpBtg3 associated nuclear protein−0.8210−610−3
5Mre11MRE11 homolog, double strand break repair nuclease−2.4710−610−3
6Rbm3RNA binding motif protein 31.0410−610−3
7Fcgr3aFc fragment of IgG receptor IIIa2.0610−610−2
8Plac8placenta associated 8 (synonym: onzin)2.8310−510−2
9Cd22CD22 molecule2.8510−510−2
10Apobec1apolipoprotein B mRNA editing enzyme catalytic subunit 11.8710−510−2
11Magee2MAGE family member E2−0.9510−510−2
12Hbb-b1hemoglobin, β adult major chain−3.9710−510−2
13Tpm2tropomyosin 21.4410−510−2
14Apobrapolipoprotein B receptor1.5610−510−2
15Cenpscentromere protein S1.6310−40.05
16GaleUDP-galactose-4-epimerase1.1510−40.05
17Pcdhb9protocadherin β9−1.0110−40.05
18P2rx4purinergic receptor P2X 41.1410−40.05
19Rn45s45S pre-ribosomal RNA−1.6210−40.05
20Nr4a3nuclear receptor subfamily 4, group A, member 3−1.2910−40.05
21Ghitmgrowth hormone inducible transmembrane protein0.4010−40.05
22Shox2short stature homeobox 26.1810−40.05
23Insig1insulin induced gene 10.4910−40.05
24Orai1ORAI calcium release-activated calcium modulator 11.8310−40.05
25Thrspthyroid hormone responsive1.4310−40.05
26Spint1serine peptidase inhibitor, Kunitz type 1−0.9110−40.05
27Liphlipase H3.2810−40.05
28Pla2g2cphospholipase A2, group IIC−1.0810−40.05
29Lilrb3lleukocyte immunoglobulin-like receptor, subfamily B, member 3-like7.3410−40.05
30Hspa1bheat shock protein family A (Hsp70) member 1B−1.2510−40.05
31Nmral1NmrA-like redox sensor 11.1810−40.05
32Mogat2monoacylglycerol O-acyltransferase 22.0810−40.05
33Defb17defensin β 176.0210−40.05
34Sh3bgrSH3 domain binding glutamate-rich protein1.1110−40.05
35Eif2b3eukaryotic translation initiation factor 2B subunit γ0.6310−40.05
36Fcrl2Fc receptor-like 21.1210−40.05
37Fuca1α-L-fucosidase 11.1010−40.05
38Bdh13-hydroxybutyrate dehydrogenase 10.3710−40.05
39Rps16ribosomal protein S161.3210−30.05
40Ifi27l2binterferon-α-inducible protein 27 like 2B2.3610−30.05
41Ifi47interferon-γ-inducible protein 471.4710−30.05
42Mcm10minichromosome maintenance 10 replication initiation factor−1.9810−30.05
43Fjx1four-jointed box kinase 10.8310−30.05
44Zmym6zinc finger MYM-type containing 6−0.5910−30.05
45Use1unconventional SNARE in the ER 11.1110−30.05
46FusFUS RNA-binding protein0.4810−30.05
Note: log2, see the legend of Figure 1; p, the statistical significance according to Fisher’s Z-test without the Benjamini correction for multiple comparisons.
Table
Table 5. The qPCR data on the selected DEGs in the hypothalamus of the independently prepared eight other tame adult male rats and eight other aggressive ones (all unrelated animals).
Table 5. The qPCR data on the selected DEGs in the hypothalamus of the independently prepared eight other tame adult male rats and eight other aggressive ones (all unrelated animals).
Study Design Behavioral “Glove” Test [20] and the qPCR Data on Gene Expression [This Work]
Rat No.12345678
Set
Glove
Test 		A−3−3−3−3−3−3−3−3
T33333333
DEGSetRelative Expression with Respect to Four Reference Genes, qPCR, M0 ± SEMTOTAL
Ascl3A0.22 ± 0.040.14 ± 0.031.04 ± 0.120.11 ± 0.030.22 ± 0.040.14 ± 0.020.12 ± 0.030.12 ± 0.030.26 ± 0.12
T1.05 ± 0.281.35 ± 0.352.05 ± 0.111.95 ± 0.322.35 ± 0.242.61 ± 0.322.08 ± 0.612.86 ± 1.102.04 ± 0.52
Apobec1A1.83 ±0.190.71 ± 0.270.30 ± 0.080.58 ± 0.170.22 ± 0.090.58 ± 0.141.86 ± 0.280.93 ± 0.160.88 ± 0.28
T2.09 ± 0.629.22 ± 0.151.12 ± 0.051.12 ± 0.074.03 ± 0.733.83 ± 0.083.49 ± 1.130.45 ± 0.113.17 ± 1.07
Defb17A0.01 ± 0.010.01 ± 0.01NDNDND0.01 ± 0.010.01 ± 0.010.01 ± 0.010.01 ± 0.01
T1.57 ± 0.221.70 ± 0.072.12 ± 0.511.20 ± 0.350.66 ± 0.041.51 ± 0.560.90 ± 0.040.99 ± 0.061.33 ± 0.35
Note. A, aggressive rats; T, tame rats; M0 ± SEM, mean ± standard error of the mean for three technical replicates; ND, not detected.
Table
Table 6. The hypothalamic DEGs of tame versus aggressive rats found in this work in comparison with their known homologous DEGs in domestic animals versus their wild congeners as reported by others.
Table 6. The hypothalamic DEGs of tame versus aggressive rats found in this work in comparison with their known homologous DEGs in domestic animals versus their wild congeners as reported by others.
#Hypothalamic DEGs, Tame vs. Aggressive RatsDEGs Within the Tissues of the Domestic Animals versus Their Wild Congeners
DEGlog2PADJTame/DomesticWild/AggressiveTissueDEGlog2PADJ[Ref]
iiiiiiivvviviiviiiixxxi
1Banp−0.8210−3rabbitsrabbitshippocampusBanp−1.390.05[28]
2Banp−0.8210−3rabbitsrabbitsparietal-temporal cortexBanp−1.2110−2[28]
3Cd222.8510−2dogswolvesbloodCd222.340.05[24]
4Cd222.8510−2foxesfoxespituitaryCd220.3210−2[23]
5Defb176.020.05rabbitsrabbitsparietal-temporal cortexDefb11.1910−2[28]
6Eif2b30.630.05guinea pigscavyfrontal cortexEif2b30.7210−3[25]
7Fcgr3a2.0610−2rabbitsrabbitsparietal-temporal cortexFcgr3b1.3510−2[28]
8Fcrl21.120.05foxesfoxespituitaryFcrl10.4310−2[23]
9Ghitm0.400.05guinea pigscavyfrontal cortexGhitm−0.580.05[25]
10Hbb-b1−3.9710−2dogswolvesbloodHbbl−5.9210−8[24]
11Hbb-b1−3.9710−2dogswolvesbloodHba1−4.0610−5[24]
12Hbb-b1−3.9710−2chickenchickenpituitaryHbad−1.0710−2[29]
13Hbb-b1−3.9710−2dogswolvesbloodHbm−6.4610−6[24]
14Hbb-b1−3.9710−2dogswolvesbloodHbz1−7.1010−2[24]
15Hspa1b−1.250.05rabbitsrabbitsparietal-temporal cortexHspa5−1.120.05[28]
16Hspa1b−1.250.05rabbitsrabbitsamygdalaHspa5−1.120.05[28]
17Hspa1b−1.250.05rabbitsrabbitsparietal-temporal cortexHspa8−1.4610−9[28]
18Hspa1b−1.250.05rabbitsrabbitsamygdalaHspa8−1.100.05[28]
19Hspa1b−1.250.05rabbitsrabbitshippocampusHspa8−1.3610−2[28]
20Ifi27l2b2.360.05chickenchickenpituitaryIfi6−2.4910−4[29]
21Krt2−1.6510−4chickenchickenpituitaryKrt17−1.120.05[29]
22Liph3.280.05guinea pigscavyfrontal cortexLipa0.8410−2[25]
23Liph3.280.05guinea pigscavyfrontal cortexLipm1.4510−2[25]
24Liph3.280.05chickenchickenpituitaryLipml0.5510−3[29]
25Mogat22.080.05rabbitsrabbitshippocampusMogat1−1.930.05[28]
26Morn11.2410−6foxesfoxespituitaryMorn2−0.250.05[23]
27Morn11.2410−6guinea pigscavyfrontal cortexMorn20.890.05[25]
28Mre11a−2.4710−3guinea pigscavyfrontal cortexMre11a1.4210−4[25]
29Nr4a3−1.2910−4chickenchickenpituitaryNr4a3−0.850.05[29]
30Nr4a3−1.2910−4rabbitsrabbitsamygdalaNr4a3−1.580.05[28]
31Nr4a3−1.2910−4guinea pigscavyfrontal cortexNr2c1−0.7410−2[25]
32Nr4a3−1.2910−4chickenchickenpituitaryNr3c10.5110−5[29]
33Nr4a3−1.2910−4guinea pigscavyfrontal cortexNr5a1−2.190.05[25]
34Orai11.830.05guinea pigscavyfrontal cortexOrai1−1.3010−3[25]
35P2rx41.140.05guinea pigscavyfrontal cortexP2rx60.550.05[25]
36Pcdhb9−1.010.054guinea pigscavyfrontal cortexPcdh20−0.730.05[25]
37Pcdhb9−1.010.054guinea pigscavyfrontal cortexPcdhac10.7210−2[25]
38Pcdhb9−1.010.054rabbitsrabbitsparietal-temporal cortexPcdhb15−1.040.05[28]
39Pcdhb9−1.010.054ratsratsfrontal cortexPcdhga12.1010−5[25]
40Pcdhb9−1.010.054rabbitsrabbitsamygdalaPcdhgb41.5310−4[28]
41Pcdhb9−1.010.054rabbitsrabbitsparietal-temporal cortexPcdhgb41.0610−4[28]
42Pcdhb9−1.010.054rabbitsrabbitshypothalamusPcdhgb41.6710−2[28]
43Pla2g2c−1.080.05rabbitsrabbitsparietal-temporal cortexPla1a1.3510−2[28]
44Pla2g2c−1.080.05guinea pigscavyfrontal cortexPla2g4a−1.7410−7[25]
45Pla2g2c−1.080.05rabbitsrabbitsparietal-temporal cortexPla2g4c2.2910−8[28]
46Pla2g2c−1.080.05rabbitsrabbitsamygdalaPla2g4c2.3410−3[28]
47Pla2g2c−1.080.05rabbitsrabbitshippocampusPla2g4c1.630.05[28]
48Pla2g2c−1.080.05guinea pigscavyfrontal cortexPla2g5−1.010.05[25]
49Pla2g2c−1.080.05chickenchickenpituitaryPla2g7−0.8310−2[29]
50Rbm31.0410−3guinea pigscavyfrontal cortexRbm111.020.05[25]
51Sh3bgr1.110.05guinea pigscavyfrontal cortexSh3bgr0.9910−2[25]
52Shox26.180.05rabbitsrabbitshippocampusShox2−3.4310−3[28]
53Spint1−0.910.05dogswolvesbloodSpint15.2810−2[24]
54Tpm21.4410−2foxesfoxespituitaryTpm40.8410−8[23]
Table
Table 7. Correlations between the significant differential gene expression in the tame versus aggressive rats under study and significant differential gene expression in domestic animals versus their wild congers presented in Table 6.
Table 7. Correlations between the significant differential gene expression in the tame versus aggressive rats under study and significant differential gene expression in domestic animals versus their wild congers presented in Table 6.
log2 ValueSigns of Log2 Values of Homologous DEGsBinomial Distributionχ2 TestFisher’s Exact Test
Homology Type PC1:
Same Signs		PC2:
Opposite Signs		χ2p
iiiiiiivvviviiviii
Number of Orthologous DEG PairsMany Homologs7010−37.7810−20.05
Only One Gene250.23
Table
Table 9. Correlations between the effects of co-directed changes in the expression of homologous genes on the human reproductive potential and on various traits during the divergence of the domestic and wild animals from their most recent common ancestor.
Table 9. Correlations between the effects of co-directed changes in the expression of homologous genes on the human reproductive potential and on various traits during the divergence of the domestic and wild animals from their most recent common ancestor.
HumanEffect of Gene Expression Changes on Human Reproductive Potential Binomial DistributionPearson’s χ2 -TestFisher’s Exact Test
Animals Decreased (→)Increased (←)χ2p
iiiiiiivvviiviiiix
Gene Expression Changes During Divergence from Most Recent Common Ancestordomestic13110−36.300.050.05
wild770.60
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Chadaeva, I.; Ponomarenko, P.; Kozhemyakina, R.; Suslov, V.; Bogomolov, A.; Klimova, N.; Shikhevich, S.; Savinkova, L.; Oshchepkov, D.; Kolchanov, N.A.; et al. Domestication Explains Two-Thirds of Differential-Gene-Expression Variance between Domestic and Wild Animals; The Remaining One-Third Reflects Intraspecific and Interspecific Variation. Animals 2021, 11, 2667. https://doi.org/10.3390/ani11092667

AMA Style

Chadaeva I, Ponomarenko P, Kozhemyakina R, Suslov V, Bogomolov A, Klimova N, Shikhevich S, Savinkova L, Oshchepkov D, Kolchanov NA, et al. Domestication Explains Two-Thirds of Differential-Gene-Expression Variance between Domestic and Wild Animals; The Remaining One-Third Reflects Intraspecific and Interspecific Variation. Animals. 2021; 11(9):2667. https://doi.org/10.3390/ani11092667

Chicago/Turabian Style

Chadaeva, Irina, Petr Ponomarenko, Rimma Kozhemyakina, Valentin Suslov, Anton Bogomolov, Natalya Klimova, Svetlana Shikhevich, Ludmila Savinkova, Dmitry Oshchepkov, Nikolay A. Kolchanov, and et al. 2021. "Domestication Explains Two-Thirds of Differential-Gene-Expression Variance between Domestic and Wild Animals; The Remaining One-Third Reflects Intraspecific and Interspecific Variation" Animals 11, no. 9: 2667. https://doi.org/10.3390/ani11092667

APA Style

Chadaeva, I., Ponomarenko, P., Kozhemyakina, R., Suslov, V., Bogomolov, A., Klimova, N., Shikhevich, S., Savinkova, L., Oshchepkov, D., Kolchanov, N. A., Markel, A., & Ponomarenko, M. (2021). Domestication Explains Two-Thirds of Differential-Gene-Expression Variance between Domestic and Wild Animals; The Remaining One-Third Reflects Intraspecific and Interspecific Variation. Animals, 11(9), 2667. https://doi.org/10.3390/ani11092667

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