Next Article in Journal
Rapid Whole Genome Sequencing Diagnoses and Guides Treatment in Critically Ill Children in Belgium in Less than 40 Hours
Next Article in Special Issue
A Swedish Familial Genome-Wide Haplotype Analysis Identified Five Novel Breast Cancer Susceptibility Loci on 9p24.3, 11q22.3, 15q11.2, 16q24.1 and Xq21.31
Previous Article in Journal
The Alpha-1 Subunit of the Na+/K+-ATPase (ATP1A1) Is a Host Factor Involved in the Attachment of Porcine Epidemic Diarrhea Virus
Previous Article in Special Issue
Molecular Investigations of Protein Aggregation in the Pathogenesis of Amyotrophic Lateral Sclerosis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 4
10.3390/ijms24043996
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Differentially Expressed Genes and Molecular Susceptibility to Human Age-Related Diseases

by
Svetlana Shikhevich
1,
Irina Chadaeva
1,
Bato Khandaev
1,2,
Rimma Kozhemyakina
1,
Karina Zolotareva
1,2,
Anna Kazachek
1,2,
Dmitry Oshchepkov
1,2,
Anton Bogomolov
1,2,
Natalya V. Klimova
1,
Vladimir A. Ivanisenko
1,2,
Pavel Demenkov
1,
Zakhar Mustafin
1,2,
Arcady Markel
1,2,
Ludmila Savinkova
1,
Nikolay A. Kolchanov
1,2,
Vladimir Kozlov
3 and
Mikhail Ponomarenko
1,*
1
Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia
2
The Natural Sciences Department, Novosibirsk State University, Novosibirsk 630090, Russia
3
Research Institute of Fundamental and Clinical Immunology (RIFCI) SB RAS, Novosibirsk 630099, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(4), 3996; https://doi.org/10.3390/ijms24043996
Submission received: 30 December 2022 / Revised: 2 February 2023 / Accepted: 13 February 2023 / Published: 16 February 2023
(This article belongs to the Special Issue Genetic and Molecular Susceptibility in Human Diseases)
Browse Figures
Versions Notes

Abstract

:
Mainstream transcriptome profiling of susceptibility versus resistance to age-related diseases (ARDs) is focused on differentially expressed genes (DEGs) specific to gender, age, and pathogeneses. This approach fits in well with predictive, preventive, personalized, participatory medicine and helps understand how, why, when, and what ARDs one can develop depending on their genetic background. Within this mainstream paradigm, we wanted to find out whether the known ARD-linked DEGs available in PubMed can reveal a molecular marker that will serve the purpose in anyone’s any tissue at any time. We sequenced the periaqueductal gray (PAG) transcriptome of tame versus aggressive rats, identified rat-behavior-related DEGs, and compared them with their known homologous animal ARD-linked DEGs. This analysis yielded statistically significant correlations between behavior-related and ARD-susceptibility-related fold changes (log2 values) in the expression of these DEG homologs. We found principal components, PC1 and PC2, corresponding to the half-sum and the half-difference of these log2 values, respectively. With the DEGs linked to ARD susceptibility and ARD resistance in humans used as controls, we verified these principal components. This yielded only one statistically significant common molecular marker for ARDs: an excess of Fcγ receptor IIb suppressing immune cell hyperactivation.

1. Introduction

Seven years ago, the World Health Organization (WHO) defined the Healthy Ageing Framework [1] and declared the decade between 2020 and 2030 as the Decade of Healthy Ageing [2]. Because a standard definition of age-related diseases (ARDs) has yet to be agreed upon, epidemiologists differentiate between, on the one hand, all non-infectious diseases with a reliance on incidence rates rising exponentially with age, no matter the lifespan, and, on the other hand, the diseases that start in early life and have stable or lowered incidence rates in the elderly [3]. Given this lack of strict classification, many age-related diseases can be named as ARDs: sarcopenic obesity [4], homeostasis dysregulation [5], subfertility [6], lipodystrophy [7], sarcopenia [8], macular degeneration [9], chronic inflammation [10], osteoarthritis [11], endothelial dysfunction [12], tissue senescence [13], cancer [14], atherosclerosis [15], cardiovascular diseases [16], chronic kidney disease [17], stroke [18], frontotemporal dementia [19], Alzheimer’s [20], and Parkinson’s [21], to mention some. Moreover, many other pathologies can contribute to ARDs: amyotrophic lateral sclerosis, to motoneuronal aging [22]; mitochondrial dysfunction, to aging as such [23]; vascular atherosclerosis, to cellular senescence [24]; hypertension, to vascular aging [25]; thalassemia, to myelodysplastic syndrome [26]; cancer, to immune system aging and vice versa [27]; and circadian rhythm disorder, to aging as such [28]. Furthermore, some diets enriched for fat [29], calcium [30], or citrates [31] can provoke ARDs, as do long-term hunger, malnutrition, anorexia, and appetite loss [32]. By contrast, moderate physical exercise [33], short-term fasting [34], and low-dose aspirin [35] can prevent such diseases.
This could be why mainstream transcriptome-profiling studies of ARD susceptibility versus ARD resistance in human volunteers [33,36,37,38,39,40,41,42,43] and animals [30,32,35,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64] are focused on the differentially expressed genes (DEGs) that are specific to gender, age, tissues, and pathogeneses. All previous research contributes to progress in predictive, preventive, personalized, participatory (4P) medicine [65] and helps us understand where, how, why, when, and what disorders can affect people depending on their genetic background, medical history, and lifestyle. Because none of us is able to escape an ARD, we expected that a meta-analysis of all available ARD-linked DEGs would eventually reveal the most common, universal theranostic molecular marker that will be permanently available in any tissue of anyone’s organism.
Because hypertension contributes to vascular aging [66] and vice versa, we have previously studied, within this mainstream paradigm, the inbred ISIAH (Inherited Stress-Induced Arterial Hypertension) rat strain [67] and sequenced transcriptomes in the brain stem [47], hypothalamus [48], renal medulla [49], renal cortex [50], and adrenal glands [51], with WAG rats used as controls. Additionally, we have recently obtained and compared transcriptomes of the midbrain tegmentum [68] in gray rats of a tame and an aggressive outbred strain [69,70,71,72]; this allowed us to identify, in in silico settings, potential molecular markers for neoteny, which has a power to reverse ARDs [73]. Furthermore, we have recently profiled transcriptomes in the hippocampus [74] of tame versus aggressive rat strains, in which deficient β-protocadherins and β-hemoglobin were found to be the statistically significantly most common theranostic molecular markers for ARD-related hypertension. That is why we sequenced, in the same way, one more transcriptome, that of the periaqueductal gray matter (PAG), in the tame versus aggressive rats, identified the corresponding behavior-related PAG-associated DEGs in them, and compared these DEGs with their homologous PubMed-based [75] ARD-linked DEGs in animals [30,32,35,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,68,74] and humans [33,36,37,38,39,40,41,42,43] to find out whether there are invariant molecular markers for such diseases among them.

2. Results

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

We focused on the PAG because the activity of this brain structure contributes to elevated pain tolerance [76], sociability, intelligence (IQ), and humor processing with increasing age [77]. In fact, our best hope was that if we had had all possible ARD molecular markers at hand, we would have eventually understand which of them might be the best helpers to relieve the suffering of ARD patients. We profiled the PAG transcriptome of three tame adult male gray rats (Rattus norvegicus) against three aggressive conspecifics on an Illumina NextSeq 550 system (see Section 4.2). The rats came from two outbred strains, one tame and one aggressive, maintained at the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Science [72,78] for more than 90 generations using the glove test [79]. The rats were not consanguineous (see Section 4.1). First, we sequenced 210,128,758 reads each 75 nt in length and deposited them in the NCBI SRA database [80] (ID PRJNA668014) (see Table 1). Next, we chose from among them 177,608,837 raw reads (84.5%) via mapping to the reference rat genome RGSC Rnor_6.0, UCSC Rn 6 July 2014 (Table 1). Then we identified 14,039 genes expressed in the PAG of the studied rats. Finally, we selected 39 DEGs using Fisher’s Z-test with the Benjamini correction for multiple comparisons and discarded hypothetical, tentative, predicted, uncharacterized, or non-protein-coding genes to minimize the false-positive error rates (Table 1 and Table 2).

2.2. Quantitative PCR (qPCR)-Based Selective Verification of the Novel PAG-Related DEGs of the Tame and Aggressive Rats

From the same two strains of rats, we took 16 additional unrelated animals: eight tame and eight aggressive, each scoring 3.5 and –3.5, respectively, on a scale spanning from –4 (most aggressive) to +4 (least aggressive) in the glove test [79] run 1 month before the PAG specimens were sampled (Table 3).
Next, from among the 39 DEGs shown in Table 2, we selected Ascl3 and Defb17 (for details, see Table 3). Table 3 contains our qPCR data on these genes in the PAG of the tame and aggressive rats (see Section 4.4). These PCR data appear as the arithmetic mean ± standard error of the mean of Ascl3 and Defb17 expression levels normalized to those of three reference genes, B2m (β-2-microglobulin) [81], Hprt1 (hypoxanthine phosphoribosyltransferase 1) [82], and Rpl30 (ribosomal protein L30 [83]), in triplicate, according to the guidelines [84]. The rightmost column of Table 3 shows arithmetic mean estimates of the expression levels of Ascl3 and Defb17 in the PAG of the tame and aggressive rats.
As can be seen from Figure 1a, both Ascl3 and Defb17 are overexpressed in the PAG of the tame rats (white bars) compared to the aggressive rats (gray bars) according to our qPCR data, this overexpression being significant (p < 0.01, double asterisk) according to both the nonparametric Mann–Whitney U test and the parametric Fisher’s Z-test.
This finding independently supports our RNA-Seq data (Table 2). Finally, Figure 1b shows Pearson’s linear correlation (r = 0.997, p < 0.005) between the log2 values (“log2” hereinafter means “the log2-transformed ratio of the expression level of a given gene between tame and aggressive rats under the given experimental conditions”) for four genes, Ascl3, Defb17, B2m, and Rpl30 (all appearing as open circles), within the RNA-Seq (x-axis) and qPCR (y-axis) datasets, both obtained here independently. This is one more piece of independent evidence in support of the relevance of our RNA-Seq data (Table 2).

2.3. Comparison of Known Animal ARD-Linked DEGs with Their Homologs among 39 Novel PAG-Related DEGs of the Tame and Aggressive Rats

We retrieved all transcriptomes of animals with ARD susceptibility and ARD resistance from the PubMed database [75] and collected 43 animal-based human ARD models (Table 4).
The total number of DEGs found in 22 original works [30,32,35,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64] was 37,834 in 17 tissues of five animal species. Figure 2 shows our procedure of comparison of 37,834 animal ARD-linked DEGs (Table 4) with the 39 PAG-related DEGs of the tame and aggressive rats (Table 2). In this figure, a Venn diagram and a table explain the procedure that led us to the following conclusion: there is a correlation between the expression of homologous genes in the tame rats and the ARD-susceptible animals. At the first step, we compiled 459 pairs of homologous DEGs, where one of 39 DEGs was taken from Table 2 and its homolog was chosen from among 37,834 DEGs in Table 4, both now being present in Table S1 (see “Supplementary Materials”). At the second step, we for the first time found the following statistically significant correlations between behavior-related and disease-susceptibility-related log2 values of the homologous animal DEGs: Pearson’s linear correlation (r = 0.13, p < 0.01), Spearman’s rank correlation (R = 0.14, p < 0.005), Kendall’s rank correlation (τ = 0.10, p < 0.0025), and the Goodman–Kruskal generalized correlation (γ = 0.10, p < 0.0025) (Figure 2). These correlations suggest a certain similarity in the expression patterns of the novel PAG-related DEGs between the tame and aggressive rats and a similarity in the expression patterns of their homologous DEGs between ARD-susceptible and ARD-resistant animals. The biological sense of these similarities was elucidated at the final step. We processed entries in Table S1 with principal component analysis in the bootstrap mode with the freely available toolbox PAST4.04 [85]. In the result, we found two principal components, major PC1 and minor PC2, corresponding to the half-sum and the half-difference of the behavior-related and ARD-susceptibility-related log2 values for the homologous animal DEGs, respectively. Here, the half-sum of the behavior- and ARD-associated log2 values, which appears as the major PC1, implies that the sense of the significant positive correlations found between these log2 values at step 2 is that their signs in the novel PAG-related rat DEGs and in the known ARD-linked animal DEGs are matching. Their formulas and 95% confidence intervals are given in the bottom part of Figure 2.

2.4. Animal ARD-Linked DEGs Are Relevant to Humans

We additionally searched for all the PubMed DEGs in ARD patients and otherwise healthy volunteers [75] (see Table 5). The total number of human ARD-linked DEGs found is 14,535 in ten tissues from 14 binary “susceptibility versus resistance” models of human ARDs (the rightmost column of Table 5) [33,36,37,38,39,40,41,42,43]. Figure 3 illustrates a modification of the previous procedure, with the animal ARD-linked DEGs replaced by their human counterparts (Table 5).
The results serve as independent control medical data (Table S2). Although no correlation was found between the behavior-related log2 values for the novel PAG-related rat DEGs and the ARD-susceptibility-related log2 values for their human homologs, their half-sum and half-difference corresponded to principal components PC1 (major) and PC2 (minor) within their quite similar 95% confidence intervals (Figure 3). This finding confirmed the results of our meta-analysis of ARD susceptibility versus ARD resistance in animals.

2.5. Searching for ARD Molecular Markers among Human Genes Orthologous to 39 Novel PAG-Related DEGs of the Tame and Aggressive Rats

First, we used the PubMed database [75] and characterized each of the 39 novel PAG-related DEGs of the tame and aggressive rats (Table 2) by answering the question as to how under- or overexpression of their human orthologs can aggravate or alleviate ARDs [86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198] (Table S3). Next, for each novel PAG-related rat DEG (Table 2), we determined the sign of the behavior-related log2 value and found how many of their homologs in the ARD-susceptible and ARD-resistant subjects have matching signs of the ARD-susceptibility-related log2 value (n = NPC1) and how many have opposite signs (n = NPC2). This is because the principal components PC1 and PC2 correspond to the half-sum and the half-difference, respectively, of these log2 values (Figure 2 and Figure 3). Table 6 shows these figures (NPC1 and NPC2) alongside their statistical significance estimates according to the binomial distribution both before (p-values) and after (PADJ values) Bonferroni’s correction for multiple comparisons.
As can be seen from this table, only one of the 39 novel DEGs in the PAG of the tame and aggressive rats is linked with PC1, namely Fcgr2b encoding Fcγ receptor IIb, which suppresses the hyperactivation of immune cells [199] (Table 7). Entries in this table suggest that the immunoregulatory DEGs homologous to rat Fcgr2b are significantly overexpressed in ARD-susceptible human and animal subjects compared to their ARD-resistant peers [35,39,43,51,55,56,58]. This leads us to propose that an excess of the immunoregulators homologous to rat Fcgr2b might be regarded as a candidate theranostic molecular marker for human ARDs.

2.6. Can Human Fcγ Receptor IIb Upregulation Serve as a Theranostic Molecular Marker for ARDs?

The following facts and hypotheses were thought to be relevant to this question: (i) the number of human infections increases when new wild animals are domesticated and become a bridge between the anthropogenic environment and wildlife [200], (ii) stray cats live longer than pet cats due to stronger disease resistance [201], and (iii) human evolutionary origin involves self-domestication [202], although this hypothesis is still debatable [203].
With this information in mind, we searched PubMed [75] and retrieved all available transcriptomes of domestic animals compared to their wild conspecifics (Table 8). As can be seen from the bottom line of Table 8, RNA-Seq data represent 2866 DEGs in eight tissues of seven domestic animal species compared to their seven wild conspecifics studied in ten original experimental works [68,74,204,205,206,207,208,209,210,211].
By looking at the entries in the left half of Table 9, it becomes clear why underexpression of the human FCGR2B gene, which is orthologous to the rat Fcgr2b gene (this being the only gene we have identified as a theranostic molecular marker for ARD; Table 9), alleviates such diseases [117,118], while its overexpression aggravates them [119,120].
From among the 39 novel PAG-related rat DEGs (Table 2) and these 2866 known animal DEGs (Table 8), we selected seven animal genes homologous to the rat Fcgr2b gene (the right half of Table 9). Furthermore, we transformed the log2 value characterizing the animal DEGs into either underexpression or overexpression of these animal genes after the domestic and wild animals split from their most recent common ancestor, according to the commonly accepted phylogeny concept [212,213,214,215,216]) (Table 9).
Downregulation of the human gene FCGR2B, which provides protection against infection, alleviates ARDs [117,118] and matches downregulation of the homologous genes Fcer2, Fcgr2b, Fcgr3a, Fcgr3b, Fcrl1, and Fcrl2 in wild rabbits [206], aggressive rats [68,204], and aggressive foxes [208] after they and their domestic conspecifics split from their most recent common ancestor (Table 9).
By contrast, an excess of this immunosuppressor in humans aggravates the cellular-senescence-related immunogenic disease [119,120] and corresponds to the excess of its homologous immunoregulators in cavies [205], domestic rabbits [206], tame rats [68,204], and tame foxes [208] during their microevolution (Table 9).
The left half of Table 10 summarizes the data shown in Table 9 in the form of a 2 × 2 contingency table, where this difference is statistically significant according to both Pearson’s χ2 test (p < 0.01) and Fisher’s exact test (p < 0.05).
In this regard, upregulation of the immunoregulators homologous to the rat Fcgr2b gene can serve as a theranostic ARD molecular marker permanently available in any tissue of anyone’s organism.

3. Discussion

3.1. Overexpression of Human Genes Homologous to the Rat Fcgr2b Gene Identified as A Molecular Marker for ARDs Is Consistent with Overexpression of Known ARD-Linked DEGs

We have found that upregulation of the human immunoregulators homologous to the rat Fcgr2b gene can serve as a theranostic ARD molecular marker permanently available in any tissue of anyone’s organism (see Table 7). As can be seen from Table 7, the immunoregulators homologous to the rat Fcgr2b gene are excessive in all tissues of all ARD-affected subjects (humans and animals) with only one exception: FCGR1B deficiency in the lungs of a patient with fibrosis in pulmonary arterial hypertension [39] (Table 7: line #12). This implies that an excess of the human immunoregulators homologous to the rat Fcgr2b gene identified in this work as being a molecular marker for age-related human diseases is consistent with the ARD-linked DEGs found independently by other authors.

3.2. Excess of Rat Fcgr2b Identified as A Molecular Marker for ARDs Is Consistent with Independent RNA-Seq Data on the Rise in the Levels of Murine Fcgr2b in Astrocytes with Age

We will discuss this result in comparison with independent RNA-Seq data [217] from a human ARD model using mice aged 7 days, 32 days, 10 weeks, 9.5 months, and 2 years. Although murine Fcgr2b was not identified as a DEG within the model in question [217], we have found a statistically significant increase in the expression of this gene in astrocytes (see Figure 4) from the hippocampus (open circles), striatum (gray circles), and cortex (black circles) of mice with increasing age. This could be regarded as an in vivo piece of independent direct evidence in support to our findings in this work.

3.3. Stabilizing Selection Preserves the Expression Pattern of the Human FCGR2B Gene Orthologous to the Rat Fcgr2b Gene, a Molecular Marker for ARDs

Because it now seems interesting to discuss how such an extraordinary expression pattern—that is, with levels increasing with age—can have been preserved, we will examine the proximal promoter of the human FCGR2B gene (see Figure S1). As can be seen from Figure S1a, the current build (No. 155) of the dbSNP database [218] contains only one single-nucleotide polymorphism, SNP rs780467580, within the 70 bp proximal promoter of the human FCGR2B gene. We have previously used our publicly available development, SNP_TATA_Comparator [219], and manually analyzed 15,080 SNPs within 1585 proximal promoters, each 70 bp in length, located upstream of the transcription start sites of protein-coding transcripts from 534 human genes (for review, see [220]). The number of SNPs within these promoters varied from one to 64 (9.51 ± 0.21; mean ± SEM), with the 95% confidence interval being between 9 and 10; therefore, the existence of only one SNP rs34166473 within the examined region of the human FCGR2B gene promoter seems to suggest a statistically significant loss of SNPs (p < 0.01, binomial distribution). It is generally recognized that the biological function of any genomic region containing such a small number of SNPs is of vital importance, and so it is preserved by natural selection [221]. This observation is consistent with the neutrality of this single SNP rs34166473 within the human FCGR2B gene promoter in question, the neutrality having been confirmed with the use of our previously developed publicly available web service SNP_TATA_Comparator [219] (see Figure S1).

3.4. The Use of Tame and Aggressive Rats as Belyaev’s Laboratory-Animal-Based Domestication Model can Represent an Adequate Domesticated-Animal-Based Model of Human ARDs

In searching for ARD molecular markers, we used tame and aggressive rats as Belyaev’s laboratory-animal-based domestication model [69,70,71,72], because we have already found molecular markers for hypertension (incidentally, this being an ARD) and potential molecular markers for neoteny (this can reverse ARDs [73]), which correspond to the hippocampal [74] and midbrain tegmental [68] transcriptomes of these rat strains. As can be seen from Table 9 and Table 10, within tissues of the domesticated compared to wild animals there is a statistically significant excess of immunoregulators homologous to rat Fcgr2b according to two independent criteria, Pearson’s χ2 test (χ2 = 7.14; p < 0.01) and Fisher’s exact test (p < 0.05). This implies that domesticated animals are susceptible to ARDs, while their wild conspecifics are not. This result is consistent with the findings reported by Fallahshahroudi and co-workers [201] that, in the wild, natural selection on animals seeks to eliminate affected individuals, while artificial animal selection during domestication does not, as it serves human needs (for example, broilers are the cheapest poultry meat, no matter whether they are susceptible or resistant to ARDs [30,34]). This is in line with our whole-genome observations suggesting that the TATA-binding protein binding sites within the gene promoters in domesticated animals have significantly more candidate SNP markers for rheumatoid polyarthritis [222] and reproductive disorders [223,224], both being ARDs, compared to their wild conspecifics. Overall, we can conclude that Belyaev’s model of domestication using tame and aggressive rats as laboratory animals seems to be applicable as one of the adequate animal-based human ARD models.

3.5. Our Focus on the PAG of Tame and Aggressive Rats Is Adequate for the Search for the ARD-Linked Rat DEGs

In this work, we focused on the PAG of the tame and aggressive rats because the activity of this brain structure contributes to elevated pain tolerance with age [77]. Our interest was to find ARD-linked rat DEGs such that, if their human homologs were taken into account, we could help reduce the suffering of patients with such diseases (see Section 2.1). Recently, a meta-analysis of the microarray datasets GSE24982, GSE63442, and GSE63651 (from the Gene Expression Omnibus (GEO) database [225]) identified the rat Fcgr2b gene as one of the six most likely hub genes responsible for neuropathic pain and aging [226], just as we found the same rat Fcgr2b gene to be a molecular marker for ARDs (see Table 2, Table 6, Table 7, Table 9, and Table 10). Apparently, this independent microarray meta-analysis result [226] favors the adequacy of our focus on the PAG of the tame versus aggressive rats in the search for ARD-linked rat DEGs.
Briefly, we used the PAG of the tame and aggressive rats and found rat Fcgr2b overexpression to be a molecular marker for elevated neuropathic pain tolerance in ARDs.

3.6. In Silico Associative Gene Network of Human Immunoregulators Homologous to the Rat Fcgr2b Gene Identified as a Molecular Marker for Pain Tolerance in ARDs

For a more detailed discussion of this correspondence between our present findings and the independent experimental data [226], see Figure 5. It presents an FCGR2B-related associative gene network as a data-mining summary of both papers and databases, which we built here using the automated mode of our publicly available web service ANDSystem [227] with “Human, FCGR2B, gene, protein” as input data. First of all, there are all six human genes in the figure, FCGR1A, FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B, homologous to the rat Fcgr2b gene identified as a molecular marker for elevated neuropathic pain tolerance in ARDs. Furthermore, there are many epigenetic regulation genes (e.g., HDAC9 for histone deacetylase 9) in line with an ortholog-based expectable age-dependent expression pattern of these immunoregulatory genes (Figure 5), which may be under stabilizing selection (Figure S1). Finally, at the bottom of this figure, are the top five diseases with the best ratings of statistical significance for the contribution of FCGR2B to their pathogenesis, namely: acute myeloid leukemia (PADJ < 10−84), rheumatoid arthritis (PADJ < 10−71), inflammation (PADJ < 10−69), systemic lupus erythematosus (PADJ < 10−67), and autoimmune diseases (PADJ < 10−60). Two of these five diseases, systemic lupus erythematosus and autoimmune diseases, were among the same top five of 174 diseases most significantly associated with the human FCGR2B gene according to the human disease database MalaCards [228] (statistical significance according to the binomial distribution; p < 0.01).

3.7. Human Immunoregulatory Genes Homologous to the Rat Fcgr2b Gene Identified as a Molecular Marker for Pain Tolerance in Age-Related Diseases Are Young on the Molecular-Evolution Scale

Because the human FCGR2B gene orthologous to the rat Fcgr2b gene identified as a molecular marker for elevated neuropathic pain tolerance in age-related diseases seems to be under stabilizing selection (Figure S1), we used our Orthoscape plug-in [229,230] within the Cytoscape software suite [231] and estimated BLAST-based [232] phylostratigraphic age indexes (PAIs) for (a) 39 human genes homologous to the behavior-related PAG-associated rat DEGs identified in this work (Table 2) and (b) 48 human FCGR2B-associated genes (Figure 5). The results are in the top and bottom parts of Table S4. As can be seen from the top part of this table, FCGR2B is one of the two youngest behavior-related human genes. Furthermore, the top ten youngest genes among all 48 human genes in its bottom part contain all six human FCGR1A, FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B genes, which is a statistically significant event according to the binomial distribution (p < 0.0001). Finally, as can be seen from Figure 6, the 48 FCGR2B-associated human genes examined are significantly younger than the 39 behavior-related human genes, according to the nonparametric Mann–Whitney U test (p < 0.01) and the parametric Fisher’s Z-test (p < 0.01).

3.8. Human Immunoregulatory Genes Homologous to the Rat Fcgr2b Gene Identified as a Molecular Marker for Pain Tolerance in ARDs can Be Permanently Available in Any Tissue of Anyone’s Organism

Human immunoregulatory genes homologous to the rat Fcgr2b gene that we have identified as a molecular marker for elevated neuropathic pain tolerance in ARDs are expressed at increased rates mostly in conventional, monocyte-derived, and plasmacytoid dendritic cells as well as macrophages [233], which occur in most tissues, where they are critical to tissue homeostasis [234]. According to the GeneCard database [235], a large number of microarray, RNA-Seq, and proteomics experiments have detected molecular products expressed from the human genes FCGR1A, FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B in the majority of the human tissues studied. Because disruptions in the cellular-senescence-associated tissue homeostasis compromise the correct activation of immune responses to pathogens and cancer cells [236,237], these human immunoregulatory genes can be permanently overexpressed in any tissue of anyone’s organism as molecular markers for elevated neuropathic pain tolerance in ARDs (see Table 7).

4. Materials and Methods

4.1. Animals

The animals used were adult male gray rats (R. norvegicus) artificially bred for over 90 generations for either aggressive or tame behavior towards humans as two outbred strains. The rats were kept under standard conditions of the Conventional Animal Facility at the ICG SB RAS (Novosibirsk, Russia), as described elsewhere [72,79,238]. The total number of rats was 22 (11 tame and 11 aggressive), each 4 months old and weighing 250–270 g, all from unrelated litters. For the gene expression analysis, all the rats were decapitated. PAG samples were excised according to a handbook technique [239], flash-frozen in liquid nitrogen, and stored at −70 °C until use. This work was conducted in line with the guidelines of the Declaration of Helsinki, of Directive 2010/63/EU of the European Parliament, and of the European Council resolution of 22 September 2010. The research protocol was approved by the Interinstitutional Commission on Bioethics at the ICG SB RAS, Novosibirsk, Russia (Approval documentation No. 8 dated 19 March 2012).

4.2. RNA-Seq

Total RNA was isolated from ~100 mg of the PAG tissue samples of tame (n = 3) and aggressive (n = 3) rats using the TRIzol™ reagent (Invitrogen, Carlsbad, CA, USA). The quality of the total-RNA samples was measured on a Bioanalyzer 2100 (Agilent, Santa-Clara, CA, USA). Samples with optimal RNA integrity numbers (RINs) were selected for further analysis. Furthermore, the total RNA was analyzed quantitatively on an Invitrogen Qubit™ 2.0 fluorometer (Invitrogen). Different RNA types were separated with the mirVana™ Kit (Thermo Fisher Scientific, Waltham, MA, USA). The Dynabeads mRNA Purification Kit (Invitrogen) was used to prepare highly purified mRNA from 5 μg of the RNA fraction depleted of small RNAs. Preparation of RNA-Seq libraries from 15–30 ng of an mRNA fraction was carried out with the help of the ScriptSeq™ v2 RNA-Seq Library Preparation Kit (epicenter®, Madison, WI, USA). The quality of the libraries obtained was examined on a Bioanalyzer 2100. After normalization, barcoded libraries were pooled and handed over 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.

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

The primary raw Fastq files were examined using a quality-control tool for high-throughput sequencing data (FastQC; https://www.bioinformatics.babraham.ac.uk/projects/fastqc; accessed on 19 December 2018). Next, we improved the quality of the raw reads using the Trimmomatic tool [240] in a step-by-step manner as follows: (i) discarded a base from either the start or end position if the quality was low; (ii) trimmed bases with a sliding-window method, and (iii) eliminated any remaining reads that were less than 36 bases in length. After that, we aligned the trimmed reads to the R. norvegicus reference genome (RGSC Rnor_6.0, UCSC version Rn6, July 2014 assembly) using the TopHat2 toolbox [241]. Next, we reformatted these alignments into sorted BAM files in SAMtools version 1.4 [242]. Then we assigned the reads in question to these genes using the htseq-count tool from the preprocessing software HTSeq v.0.7.2 [243] along with gtf files containing the coordinates of the rat genes according to Rnor_6.0 and an indexed SAM file. Finally, we used DESeq2 [244] via the web service IRIS (http://bmbl.sdstate.edu/IRIS/; accessed on 16 January 2020), rated differential expression levels of the rat genes, and, to minimize false-positive error rates, applied Fisher’s Z-test [245] with the Benjamini correction for multiple comparisons as well as discarded all hypothetical, tentative, predicted, uncharacterized, and non-protein-coding genes.

4.4. qPCR

To examine independently and selectively the novel tame versus aggressive rat PAG DEGs identified here (Table 2), we performed a qPCR control assay on the total RNA taken from the remaining samples of the PAG of tame (n = 8) and aggressive (n = 8) rats. First, we isolated total RNA using TRIzol™, purified it on Agencourt RNAClean XP Kit magnetic beads (Beckman, #A63987), and quantified it using a Qubit™ 2.0 fluorometer (Invitrogen/Life Technologies) and a Qubit™ RNA High-Sensitivity Assay Kit (Invitrogen, cat. # Q32852). Next, we synthesized cDNA using the Reverse Transcription Kit (Syntol, #OT-1). Finally, we designed oligonucleotide primers for qPCR using the web service PrimerBLAST [246] (Table 11).
After that, we conducted qPCR on a LightCycler® 96 (Roche, Basel, Basel-Stadt, Switzerland) with the EVA Green I Kit in three technical replicates. We estimated qPCR efficiency using serial cDNA dilutions (standards). Following the recommendations set out in the MIQE guidelines [84], we analyzed three reference genes at once: B2m (β-2-microglobulin) [81], Hprt1 (hypoxanthine phosphoribosyltransferase 1) [82], and Rpl30 (ribosomal protein L30) [83].

4.5. DEGs

We have analyzed all publicly available independent experimental RNA-Seq datasets of the transcriptomes of tissues from ARD-susceptible versus ARD-resistant subjects (humans [33,36,37,38,39,40,41,42,43] and animals [30,32,35,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64]) and the transcriptomes of tissues from domestic versus wild animals [68,74,204,205,206,207,208,209,210,211].

4.6. Human Genes

We analyzed the 39 human genes that are orthologous to the 39 PAG-related DEGs of the tame and aggressive rats (Table 2). With reliance on PubMed [75], we gathered clinical information about whether downregulation or upregulation of each of these 39 human genes can alleviate or aggravate ARDs (Table 9 and Table S3).

4.7. DNA Sequences

For in silico analysis of the human genes encoding the novel candidate molecular markers for ARDs, we retrieved the DNA sequences and SNPs of their 70 bp proximal promoters from the Ensembl database [247] and the dbSNP database [218], respectively, using the UCSC Genome Browser (reference human genome assembly GRCh38/hg38) [248] in both dialog mode and automated mode using the Bioperl toolkit [249] (Figure S1).

4.8. In Silico Analysis of DNA Sequences

We analyzed SNPs within DNA sequences using our previously published publicly available web service SNP_TATA_Comparator [219], which implements our bioinformatic model of three-step binding of TATA-binding protein (TBP) to a given 70 bp proximal promoter of the human gene under study, as detailed in the Supplementary Materials (Section S1 “Supplementary methods for DNA sequence analysis”) [250,251,252,253,254,255,256,257,258,259,260,261,262] and additionally illustrated in Figure S1.

4.9. A Knowledge Base for Domestic Animal DEGs Whose Human Orthologs Can Affect ARD Severity

In files with the flat Excel-compatible textual format, we have documented all proposed associations between the domestic and wild animal DEGs homologous to the 39 novel DEGs that we found in the PAG of the tame and aggressive rats. We have also documented how downregulation or upregulation of the human genes homologous to these PAG-related rat DEGs can affect ARD severity. Finally, we have added our current findings to our previously published public PetDEGsDB knowledge base, its new build being freely available at www.sysbio.ru/domestic-wild (accessed on 14 December 2022) in the MariaDB 10.2.12 database management system (MariaDB Corp AB, Espoo, Finland).

4.10. Data Mining of Literature Sources and Databases Publicly Available on the Internet

We conducted data mining using the automated mode of our previously published freely available web service ANDSystem [227], with “Human, FCGR2B, gene, protein” as input keywords, with all the other parameters set at their default values.

4.11. In Silico Estimation of the BLAST-Based PAIs of a Given Human Gene

We estimated the BLAST-based [232] PAIs for a given human gene whose NCBI Entrez gene number served as input for our Orthoscape plug-in [229,230] within the Cytoscape software suite [231]. The output was the most recent common ancestor of all the animal species whose DNA sequence of this gene is already known. The following evolutionary rank scale was used: 0, Cellular organisms; 1, Eukaryota; 2, Opisthokonta; 3, Metazoa; 4, Eumetazoa; 5, Bilateria; 6, Deuterostomia; 7, Chordata; 8, Craniata; 9, Vertebrata; 10, Gnathostomata; 11, Teleostomi; 12, Euteleostomi; 13, Sarcopterygii; 14, Dipnotetrapodomorpha; 15, Tetrapoda; 16, Amniota; 17, Mammalia; 18, Theria; 19, Eutheria; 20, Euarchontoglires; 21, Primates; 22, Haplorrhini; 23, Simiiformes; 24, Catarrhini; 25, Hominoidea; 26, Hominidae; 27, Homininae; and 28, Homo.

4.12. Statistical Analysis

We performed the Mann–Whitney U test, Fisher’s Z-test, Pearson’s linear correlation test, Goodman–Kruskal generalized correlation test, Spearman’s and Kendall’s rank correlation tests, Pearson’s χ2 test, Fisher’s exact test, and the binomial-distribution analysis using appropriate options in the standard software STATISTICA (StatsoftTM). Furthermore, using the PAST4.04 software package [85], we conducted a principal component analysis in the bootstrap refinement mode via its mode selection path “Multivariate”→”Ordination”→“Principal Components (PCA)”→“Correlation”→“Bootstrap.”

5. Conclusions

First, we have profiled the PAG transcriptome in three tame adult male rats versus three aggressive conspecifics, all animals being unrelated, and made the primary raw reads publicly available (NCBI SRA database ID: PRJNA668014) [80]. With the use of this transcriptome, we have found 39 PAG-related DEGs whose statistical significance (PADJ < 0.05, Fisher’s Z-test with the Benjamini correction for multiple comparisons) was acceptable (Table 2). We have selectively verified these DEGs with independent experimental analyses (qPCR) of eight other tame and eight other aggressive adult male rats from unrelated litters of the same two outbred strains (Table 3 and Figure 1).
Secondly, we have found that Fcγ receptor IIb overexpression is a statistically significant molecular marker for ARDs. To come to this conclusion, we compared 39 novel DEGs in the PAG of tame and aggressive rats to their known homologs associated with ARDs in animals and humans, using correlation and principal component analysis, as well as Bonferroni’s correction for multiple comparisons.
Finally, we propose the human immunoregulatory genes FCGR1A, FCGR2A, FCGR2B, FCGR2C, FCGR3A, and FCGR3B homologous to the rat Fcgr2b gene as theranostic molecular markers for age-related diseases.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24043996/s1.

Author Contributions

Conceptualization and supervision, N.A.K. and V.K.; methodology, A.M.; investigation, I.C., R.K., N.V.K., and S.S.; software, A.B.; validation, L.S.; resources, D.O., V.A.I., P.D., and Z.M.; data curation, K.Z., A.K., and B.K.; 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 Federal Science and Technology Program for the Development of Genetic Technologies.

Institutional Review Board Statement

This work was conducted in line with the guidelines of the Declaration of Helsinki, of Directive 2010/63/EU of the European Parliament, and of the European Council resolution of 22 September 2010. The research protocol was approved by the Interinstitutional Commission on Bioethics at the ICG 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 also thankful to the multi-access bioinformatics center for the use of computational resources as supported by Russian government project No. FWNR-2022-0020.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ARDage-related disease
DEGdifferentially expressed gene
log2 valuelog2-transformed gene expression fold change
PAGperiaqueductal gray matter
PAIphylostratigraphic age index
PC1 (PC2)major (minor) principal component
qPCRquantitative polymerase chain reaction
RNA-SeqRNA sequencing
SNPsingle-nucleotide polymorphism
TBPTATA-binding protein
WHOWorld Health Organization

References

  1. Byles, J. The 45 and up study: An investment in healthy ageing. Public. Health Res. Pract. 2022, 32, 3242231. [Google Scholar] [CrossRef] [PubMed]
  2. Lum, T.Y.S. The pathways to healthy ageing: Evidence from longitudinal studies and real-time data. Innov. Aging. 2021, 5, 467. [Google Scholar] [CrossRef]
  3. Le Couteur, D.G.; Thillainadesan, J. What is an aging-related disease? An epidemiological perspective. J. Gerontol. A. Biol. Sci. Med. Sci. 2022, 77, 2168–2174. [Google Scholar] [CrossRef] [PubMed]
  4. Nunan, E.; Wright, C.L.; Semola, O.A.; Subramanian, M.; Balasubramanian, P.; Lovern, P.C.; Fancher, I.S.; Butcher, J.T. Obesity as a premature aging phenotype–Implications for sarcopenic obesity. Geroscience 2022, 44, 1393–1405. [Google Scholar] [CrossRef] [PubMed]
  5. Yue, Z.; Nie, L.; Zhang, P.; Chen, Q.; Lv, Q.; Wang, Q. Tissue-resident macrophage inflammaging aggravates homeostasis dysregulation in age-related diseases. Cell. Immunol. 2021, 361, 104278. [Google Scholar] [CrossRef]
  6. Deryabin, P.I.; Borodkina, A.V. Epigenetic clocks provide clues to the mystery of uterine ageing. Hum. Reprod. Update 2022, dmac042. [Google Scholar] [CrossRef]
  7. Zammouri, J.; Vatier, C.; Capel, E.; Auclair, M.; Storey-London, C.; Bismuth, E.; Mosbah, H.; Donadille, B.; Janmaat, S.; Feve, B.; et al. Molecular and cellular bases of lipodystrophy syndromes. Front. Endocrinol. 2022, 12, 803189. [Google Scholar] [CrossRef]
  8. Zhang, X.; Li, H.; He, M.; Wang, J.; Wu, Y.; Li, Y. Immune system and sarcopenia: Presented relationship and future perspective. Exp. Gerontol. 2022, 164, 111823. [Google Scholar] [CrossRef]
  9. Armstrong, G.W.; Miller, J.B. Telemedicine for the diagnosis and management of age-related macular degeneration: A review. J. Clin. Med. 2022, 11, 835. [Google Scholar] [CrossRef]
  10. Gomez, C.R. Role of heat shock proteins in aging and chronic inflammatory diseases. Geroscience 2021, 43, 2515–2532. [Google Scholar] [CrossRef]
  11. Boulestreau, J.; Maumus, M.; Jorgensen, C.; Noel, D. Extracellular vesicles from mesenchymal stromal cells: Therapeutic perspectives for targeting senescence in osteoarthritis. Adv. Drug. Deliv. Rev. 2021, 175, 113836. [Google Scholar] [CrossRef]
  12. Ting, K.K.; Coleman, P.; Zhao, Y.; Vadas, M.A.; Gamble, J.R. The aging endothelium. Vasc. Biol. 2021, 3, R35–R47. [Google Scholar] [CrossRef] [PubMed]
  13. Tuttle, C.S.L.; Luesken, S.W.M.; Waaijer, M.E.C.; Maier, A.B. Senescence in tissue samples of humans with age-related diseases: A systematic review. Ageing. Res. Rev. 2021, 68, 101334. [Google Scholar] [CrossRef] [PubMed]
  14. Martins, W.K.; Belotto, R.; Silva, M.N.; Grasso, D.; Suriani, M.D.; Lavor, T.S.; Itri, R.; Baptista, M.S.; Tsubone, T.M. Autophagy regulation and photodynamic therapy: Insights to improve outcomes of cancer treatment. Front Oncol. 2021, 10, 610472. [Google Scholar] [CrossRef] [PubMed]
  15. Kishimoto, Y.; Kondo, K.; Momiyama, Y. The protective role of sestrin2 in atherosclerotic and cardiac diseases. Int. J. Mol. Sci. 2021, 22, 1200. [Google Scholar] [CrossRef] [PubMed]
  16. Sutton, N.R.; Malhotra, R.; St Hilaire, C.; Aikawa, E.; Blumenthal, R.S.; Gackenbach, G.; Goyal, P.; Johnson, A.; Nigwekar, S.U.; Shanahan, C.M.; et al. Molecular mechanisms of vascular health: Insights from vascular aging and calcification. Arterioscler. Thromb. Vasc. Biol. 2023, 43, 15–29. [Google Scholar] [CrossRef]
  17. Figuer, A.; Bodega, G.; Tato, P.; Valera, G.; Serroukh, N.; Ceprian, N.; de Sequera, P.; Morales, E.; Carracedo, J.; Ramirez, R.; et al. Premature aging in chronic kidney disease: The outcome of persistent inflammation beyond the bounds. Int. J. Environ. Res. Public. Health. 2021, 18, 8044. [Google Scholar] [CrossRef]
  18. Li, X.; Gao, X.; Zhang, W.; Liu, M.; Han, Z.; Li, M.; Lei, P.; Liu, Q. Microglial replacement in the aged brain restricts neuroinflammation following intracerebral hemorrhage. Cell. Death. Dis. 2022, 13, 33. [Google Scholar] [CrossRef]
  19. Jicha, G.A. Medical management of frontotemporal dementias: The importance of the caregiver in symptom assessment and guidance of treatment strategies. J. Mol. Neurosci. 2011, 45, 713–723. [Google Scholar] [CrossRef] [Green Version]
  20. Schramm, C.; Wallon, D.; Nicolas, G.; Charbonnier, C. What contribution can genetics make to predict the risk of Alzheimer’s disease? Rev. Neurol. 2022, 178, 414–421. [Google Scholar] [CrossRef]
  21. Bohnen, N.I.; Yarnall, A.J.; Weil, R.S.; Moro, E.; Moehle, M.S.; Borghammer, P.; Bedard, M.A.; Albin, R.L. Cholinergic system changes in Parkinson’s disease: Emerging therapeutic approaches. Lancet. Neurol. 2022, 21, 381–392. [Google Scholar] [CrossRef]
  22. Ni, Y.Q.; Xu, H.; Liu, Y.S. Roles of long non-coding RNAs in the Development of aging-related neurodegenerative diseases. Front. Mol. Neurosci. 2022, 15, 844193. [Google Scholar] [CrossRef] [PubMed]
  23. Schroth, J.; Henson, S.M. Mitochondrial dysfunction accelerates ageing. Immunometabolism 2020, 2, e200035. [Google Scholar] [CrossRef] [PubMed]
  24. Wang, J.C.; Bennett, M. Aging and atherosclerosis: Mechanisms, functional consequences, and potential therapeutics for cellular senescence. Circ. Res. 2012, 111, 245–259. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Schreckenberger, Z.J.; Wenceslau, C.F.; Joe, B.; McCarthy, C.G. Mitophagy in hypertension-associated premature vascular aging. Am. J. Hypertens. 2020, 33, 804–812. [Google Scholar] [CrossRef] [PubMed]
  26. Liu, C.; Zou, C.; Zou, S.; Wang, Q.; Xiao, D.; Zhang, L. Abnormal hemoglobin H band in myelodysplastic syndromes (MDS): A case report. Transfus. Clin. Biol. 2021, 28, 206–210. [Google Scholar] [CrossRef]
  27. Mandelblatt, J. Squamous cell cancer of the cervix, immune senescence and HPV: Is cervical cancer an age-related neoplasm? Adv. Exp. Med. Biol. 1993, 330, 13–26. [Google Scholar] [PubMed]
  28. Manoogian, E.N.C.; Panda, S. Circadian rhythms, time-restricted feeding, and healthy aging. Ageing Res. Rev. 2017, 39, 59–67. [Google Scholar] [CrossRef]
  29. Longo, V.D.; Anderson, R.M. Nutrition, longevity and disease: From molecular mechanisms to interventions. Cell 2022, 185, 1455–1470. [Google Scholar] [CrossRef]
  30. Park, W.; Rengaraj, D.; Kil, D.Y.; Kim, H.; Lee, H.K.; Song, K.D. RNA-seq analysis of the kidneys of broiler chickens fed diets containing different concentrations of calcium. Sci. Rep. 2017, 7, 11740. [Google Scholar] [CrossRef]
  31. Mycielska, M.E.; James, E.N.; Parkinson, E.K. Metabolic alterations in cellular senescence: The role of citrate in ageing and age-related disease. Int. J. Mol. Sci. 2022, 23, 3652. [Google Scholar] [CrossRef] [PubMed]
  32. de Souto Barreto, P.; Cesari, M.; Morley, J.E.; Roberts, S.; Landi, F.; Cederholm, T.; Rolland, Y.; Vellas, B.; Fielding, R. Appetite loss and anorexia of aging in clinical care: An ICFSR task force report. J. Frailty. Aging. 2022, 11, 129–134. [Google Scholar] [CrossRef] [PubMed]
  33. Melov, S.; Tarnopolsky, M.A.; Beckman, K.; Felkey, K.; Hubbard, A. Resistance exercise reverses aging in human skeletal muscle. PLoS ONE 2007, 2, e465. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Zhang, T.; Chen, Y.; Wen, J.; Jia, Y.; Wang, L.; Lv, X.; Yang, W.; Qu, C.; Li, H.; Wang, H.; et al. Transcriptomic analysis of laying hens revealed the role of aging-related genes during forced molting. Genes 2021, 12, 1767. [Google Scholar] [CrossRef] [PubMed]
  35. Liu, X.; Zhan, Y.; Xu, W.; Liu, L.; Liu, X.; Da, J.; Zhang, K.; Zhang, X.; Wang, J.; Liu, Z.; et al. Characterization of transcriptional landscape in bone marrow-derived mesenchymal stromal cells treated with aspirin by RNA-seq. PeerJ 2022, 10, e12819. [Google Scholar] [CrossRef]
  36. Wu, Y.B.; Zang, W.D.; Yao, W.Z.; Luo, Y.; Hu, B.; Wang, L.; Liang, Y.L. Analysis of FOS, BTG2, and NR4A in the function of renal medullary hypertension. Genet. Mol. Res. 2013, 12, 3735–3741. [Google Scholar] [CrossRef]
  37. Li, C.; Zhang, Z.; Xu, Q.; Wu, T.; Shi, R. Potential mechanisms and serum biomarkers involved in sex differences in pulmonary arterial hypertension. Medicine 2020, 99, e19612. [Google Scholar] [CrossRef]
  38. Stearman, R.S.; Bui, Q.M.; Speyer, G.; Handen, A.; Cornelius, A.R.; Graham, B.B.; Kim, S.; Mickler, E.A.; Tuder, R.M.; Chan, S.Y.; et al. Systems analysis of the human pulmonary arterial hypertension lung transcriptome. Am. J. Respir. Cell Mol. Biol. 2019, 60, 637–649. [Google Scholar] [CrossRef]
  39. Yao, X.; Jing, T.; Wang, T.; Gu, C.; Chen, X.; Chen, F.; Feng, H.; Zhao, H.; Chen, D.; Ma, W. Molecular characterization and elucidation of pathways to identify novel therapeutic targets in pulmonary arterial hypertension. Front. Physiol. 2021, 12, 694702. [Google Scholar] [CrossRef]
  40. Neusser, M.A.; Lindenmeyer, M.T.; Moll, A.G.; Segerer, S.; Edenhofer, I.; Sen, K.; Stiehl, D.P.; Kretzler, M.; Grone, H.J.; Schlondorff, D.; et al. Human nephrosclerosis triggers a hypoxia-related glomerulopathy. Am. J. Pathol. 2010, 176, 594–607. [Google Scholar] [CrossRef] [Green Version]
  41. Zheng, Y.; He, J.Q. Common differentially expressed genes and pathways correlating both coronary artery disease and atrial fibrillation. EXCLI J. 2021, 20, 126–141. [Google Scholar] [PubMed]
  42. Sproviero, D.; Gagliardi, S.; Zucca, S.; Arigoni, M.; Giannini, M.; Garofalo, M.; Fantini, V.; Pansarasa, O.; Avenali, M.; Ramusino, M.C.; et al. Extracellular vesicles derived from plasma of patients with neurodegenerative disease have common transcriptomic profiling. Front. Aging. Neurosci. 2022, 14, 785741. [Google Scholar] [CrossRef] [PubMed]
  43. Liu, Y.; Reynolds, L.M.; Ding, J.; Hou, L.; Lohman, K.; Young, T.; Cui, W.; Huang, Z.; Grenier, C.; Wan, M.; et al. Blood monocyte transcriptome and epigenome analyses reveal loci associated with human atherosclerosis. Nat. Commun. 2017, 8, 393. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Stefanova, N.A.; Maksimova, K.Y.; Rudnitskaya, E.A.; Muraleva, N.A.; Kolosova, N.G. Association of cerebrovascular dysfunction with the development of Alzheimer’s disease-like pathology in OXYS rats. BMC Genom. 2018, 19, 75. [Google Scholar] [CrossRef] [PubMed]
  45. Stefanova, N.A.; Ershov, N.I.; Maksimova, K.Y.; Muraleva, N.A.; Tyumentsev, M.A.; Kolosova, N.G. The rat prefrontal-cortex transcriptome: Effects of aging and sporadic Alzheimer’s disease-like pathology. J. Gerontol. A Biol. Sci. Med. Sci. 2019, 74, 33–43. [Google Scholar] [CrossRef]
  46. Kozhevnikova, O.S.; Korbolina, E.E.; Ershov, N.I.; Kolosova, N.G. Rat retinal transcriptome: Effects of aging and AMD-like retinopathy. Cell Cycle 2013, 12, 1745–1761. [Google Scholar] [CrossRef] [Green Version]
  47. Fedoseeva, L.A.; Klimov, L.O.; Ershov, N.I.; Efimov, V.M.; Markel, A.L.; Orlov, Y.L.; Redina, O.E. The differences in brain stem transcriptional profiling in hypertensive ISIAH and normotensive WAG rats. BMC Genom. 2019, 20, 297. [Google Scholar] [CrossRef] [Green Version]
  48. Klimov, L.O.; Ershov, N.I.; Efimov, V.M.; Markel, A.L.; Redina, O.E. Genome-wide transcriptome analysis of hypothalamus in rats with inherited stress-induced arterial hypertension. BMC. Genet. 2016, 17, 13. [Google Scholar] [CrossRef] [Green Version]
  49. Ryazanova, M.A.; Fedoseeva, L.A.; Ershov, N.I.; Efimov, V.M.; Markel, A.L.; Redina, O.E. The gene-expression profile of renal medulla in ISIAH rats with inherited stress-induced arterial hypertension. BMC Genet 2016, 17, 151. [Google Scholar] [CrossRef] [Green Version]
  50. Fedoseeva, L.A.; Ryazanova, M.A.; Ershov, N.I.; Markel, A.L.; Redina, O.E. Comparative transcriptional profiling of renal cortex in rats with inherited stress-induced arterial hypertension and normotensive Wistar Albino Glaxo rats. BMC Genet. 2016, 17, 12. [Google Scholar] [CrossRef] [Green Version]
  51. Fedoseeva, L.A.; Klimov, L.O.; Ershov, N.I.; Alexandrovich, Y.V.; Efimov, V.M.; Markel, A.L.; Redina, O.E. Molecular determinants of the adrenal gland functioning related to stress-sensitive hypertension in ISIAH rats. BMC Genom. 2016, 17, 989. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Yuan, X.; Wu, Q.; Liu, X.; Zhang, H.; Xiu, R. Transcriptomic profile analysis of brain microvascular pericytes in spontaneously hypertensive rats by RNA-Seq. Am. J. Transl. Res. 2018, 10, 2372–2386. [Google Scholar]
  53. Liddelow, S.A.; Dziegielewska, K.M.; Ek, C.J.; Habgood, M.D.; Bauer, H.; Bauer, H.C.; Lindsay, H.; Wakefield, M.J.; Strazielle, N.; Kratzer, I.; et al. Mechanisms that determine the internal environment of the developing brain: A transcriptomic, functional and ultrastructural approach. PLoS ONE 2013, 8, e65629. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Chen, Z.; Huang, Z.; Zhao, X.; Zhou, Y.; Zhang, P.; Li, Y. Transcriptome analysis of differentially expressed genes involved in the inflammageing status of gingiva in aged mice. Oral. Dis. 2022. [Google Scholar] [CrossRef] [PubMed]
  55. Kaya, S.; Schurman, C.A.; Dole, N.S.; Evans, D.S.; Alliston, T. Prioritization of genes relevant to bone fragility through the unbiased integration of aging mouse bone transcriptomics and human GWAS analyses. J. Bone. Miner. Res. 2022, 37, 804–817. [Google Scholar] [CrossRef] [PubMed]
  56. Wang, Y.; Eng, D.G.; Pippin, J.W.; Gharib, S.A.; McClelland, A.; Gross, K.W.; Shankland, S.J. Sex differences in transcriptomic profiles in aged kidney cells of renin lineage. Aging 2018, 10, 606–621. [Google Scholar] [CrossRef] [Green Version]
  57. Li, J.; Gao, F.; Wei, L.; Chen, L.; Qu, N.; Zeng, L.; Luo, Y.; Huang, X.; Jiang, H. Predict the role of lncRNA in kidney aging based on RNA sequencing. BMC Genom. 2022, 23, 254. [Google Scholar] [CrossRef]
  58. Gorr, M.W.; Francois, A.; Marcho, L.M.; Saldana, T.; McGrail, E.; Sun, N.; Stratton, M.S. Molecular signature of cardiac remodeling associated with Polymerase Gamma mutation. Life Sci. 2022, 298, 120469. [Google Scholar] [CrossRef]
  59. Kiss, T.; Nyul-Tooth, A.; Gulej, R.; Tarantini, S.; Csipo, T.; Mukli, P.; Ungvari, A.; Balasubramanian, P.; Yabluchanskiy, A.; Benyo, Z.; et al. Old blood from heterochronic parabionts accelerates vascular aging in young mice: Transcriptomic signature of pathologic smooth muscle remodeling. Geroscience 2022, 44, 953–981. [Google Scholar] [CrossRef]
  60. Fang, K.; Liu, D.; Pathak, S.S.; Yang, B.; Li, J.; Karthikeyan, R.; Chao, O.Y.; Yang, Y.M.; Jin, V.X.; Cao, R. Disruption of circadian rhythms by ambient light during neurodevelopment leads to autistic-like molecular and behavioral alterations in adult mice. Cells 2021, 10, 3314. [Google Scholar] [CrossRef]
  61. Myers, M.J.; Shaik, F.; Shaik, F.; Always, S.E.; Mohamed, J.S. Skeletal muscle gene expression profile in response to caloric restriction and aging: A role for SirT1. Genes 2021, 12, 691. [Google Scholar] [CrossRef] [PubMed]
  62. Puig, O.; Wang, I.M.; Cheng, P.; Zhou, P.; Roy, S.; Cully, D.; Peters, M.; Benita, Y.; Thompson, J.; Cai, T.Q. Transcriptome profiling and network analysis of genetically hypertensive mice identifies potential pharmacological targets of hypertension. Physiol. Genomics. 2010, 42, 24–32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Loke, S.Y.; Wong, P.T.; Ong, W.Y. Global gene expression changes in the prefrontal cortex of rabbits with hypercholesterolemia and/or hypertension. Neurochem. Int. 2017, 102, 33–56. [Google Scholar] [CrossRef] [PubMed]
  64. da Costa Silva, J.R.; Fujimura, P.T.; Batista, L.L.; Malta, S.M.; Filho, R.M.; Silva, M.H.; de Souza, A.G.; Silva, A.P.M.; Borges, L.D.F.; Bastos, V.A.F.; et al. Differential gene expression by RNA-seq during Alzheimer’s disease-like progression in the Drosophila melanogaster model. Neurosci. Res. 2022, 180, 1–12. [Google Scholar] [CrossRef] [PubMed]
  65. Trovato, G.M. Sustainable medical research by effective and comprehensive medical skills: Overcoming the frontiers by predictive, preventive and personalized medicine. EPMA. J. 2014, 5, 14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Litwin, M.; Feber, J. Origins of primary hypertension in children: Early vascular or biological aging? Hypertension 2020, 76, 1400–1409. [Google Scholar] [CrossRef]
  67. Markel, A.L. Features of the behavior of the rat with hereditarily determined arterial hypertension. Zh. Vyssh. Nerv. Deiat. Im. IP. Pavlov. 1986, 36, 956–962. [Google Scholar]
  68. Oshchepkov, D.; Chadaeva, I.; Kozhemyakina, R.; Shikhevich, S.; Sharypova, E.; Savinkova, L.; Klimova, N.V.; Tsukanov, A.; Levitsky, V.G.; Markel, A.L. Transcription factors as important regulators of changes in behavior through domestication of gray rats: Quantitative data from RNA sequencing. Int. J. Mol. Sci. 2022, 23, 12269. [Google Scholar] [CrossRef]
  69. Herbek, Y.E.; Zakharov, I.K.; Trapezov, O.V.; Shumny, V.K. Evolution compressed in time. Philos. Sci. 2013, 1, 115–139. [Google Scholar]
  70. Oskina, I.N.; Herbeck, Y.E.; Shikhevich, S.G.; Plyusnina, I.Z.; Gulevich, R.G. Alterations in the hypothalamus-pituitary-adrenal and immune systems during selection of animals for tame behavior. Information. Bulletin. VOGiS 2008, 12, 39–49. [Google Scholar]
  71. Prasolova, L.A.; Gerbek, Y.E.; Gulevich, R.G.; Shikhevich, S.G.; Konoshenko, M.Y.; Kozhemyakina, R.V.; Oskina, I.N.; Plyusnina, I.Z. The effects of prolonged selection for behavior on the stress response and activity of the reproductive system of male grey mice (Rattus norvegicus). Russ. J. Genet. 2014, 50, 846–852. [Google Scholar] [CrossRef]
  72. 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]
  73. Melamed, D.; Scott, D.W. Aging and neoteny in the B lineage. Blood 2012, 120, 4143–4149. [Google Scholar] [CrossRef] [Green Version]
  74. Oshchepkov, D.; Chadaeva, I.; Kozhemyakina, R.; Zolotareva, K.; Khandaev, B.; Sharypova, E.; Ponomarenko, P.; Bogomolov, A.; Klimova, N.V.; Shikhevich, S.; et al. Stress reactivity, susceptibility to hypertension, and differential expression of genes in hypertensive compared to normotensive patients. Int. J. Mol. Sci. 2022, 23, 2835. [Google Scholar] [CrossRef] [PubMed]
  75. Lu, Z. PubMed and beyond: A survey of web tools for searching biomedical literature. Database 2011, 2011, baq036. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Petitjean, H.; Fatima, T.; Mouchbahani-Constance, S.; Davidova, A.; Ferland, C.E.; Orlowski, J.; Sharif-Naeini, R. Loss of SLC9A6/NHE6 impairs nociception in a mouse model of Christianson syndrome. Pain 2020, 161, 2619–2628. [Google Scholar] [CrossRef] [PubMed]
  77. Vrticka, P.; Black, J.M.; Neely, M.; Walter Shelly, E.; Reiss, A.L. Humor processing in children: Influence of temperament, age and IQ. Neuropsychologia 2013, 51, 2799–2811. [Google Scholar] [CrossRef]
  78. Osadchuk, A.V.; Markel, A.L.; Khusainov, R.A.; 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. Sov. Genet. 1979, 15, 1847–1857. [Google Scholar]
  79. 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]
  80. Sayers, E.W.; Beck, J.; Bolton, E.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. 2021, 49, D10–D17. [Google Scholar] [CrossRef]
  81. 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]
  82. 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] [PubMed]
  83. 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] [PubMed]
  84. Bustin, S.A.; Benes, V.; Garson, J.A.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M.W.; Shipley, G.L.; et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 2009, 55, 611–622. [Google Scholar] [CrossRef] [Green Version]
  85. Hammer, O.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 2001, 4, 1–9. [Google Scholar]
  86. Byman, E.; Nagga, K.; Gustavsson, A.M.; Andersson-Assarsson, J.; Hansson, O.; Sonestedt, E.; Wennstrom, M.; Netherlands Brain Bank. Alpha-amylase 1A copy number variants and the association with memory performance and Alzheimer’s dementia. Alzheimers. Res. Ther 2020, 12, 158. [Google Scholar] [CrossRef]
  87. Alzheimer’s Association. Alzheimer’s disease facts and figures. Alzheimers. Dement. 2021, 17, 327–406. [Google Scholar]
  88. Wu, J.; Wei, Y.; Li, T.; Lin, L.; Yang, Z.; Ye, L. DNA methylation-mediated lowly expressed AOX1 promotes cell migration and invasion of prostate cancer. Urol. Int. 2022, 9, e522634. [Google Scholar] [CrossRef]
  89. Freeland, J.; Crowell, P.D.; Giafaglione, J.M.; Boutros, P.C.; Goldstein, A.S. Aging of the progenitor cells that initiate prostate cancer. Cancer Lett. 2021, 515, 28–35. [Google Scholar] [CrossRef]
  90. Turnell, B.R.; Kumpitsch, L.; Reinhardt, K. Production and scavenging of reactive oxygen species both affect reproductive success in male and female Drosophila melanogaster. Biogerontology 2021, 22, 379–396. [Google Scholar] [CrossRef]
  91. Wang, C.Y.; Shahi, P.; Huang, J.T.; Phan, N.N.; Sun, Z.; Lin, Y.C.; Lai, M.D.; Werb, Z. Systematic analysis of the achaete-scute complex-like gene signature in clinical cancer patients. Mol. Clin. Oncol. 2017, 6, 7–18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Sarkozy, C.; Salles, G.; Falandry, C. The biology of aging and lymphoma: A complex interplay. Curr. Oncol. Rep. 2015, 17, 32. [Google Scholar] [CrossRef] [PubMed]
  93. Hu, C.; Beebe, K.; Hernandez, E.J.; Lazaro-Guevara, J.M.; Revelo, M.P.; Huang, Y.; Maschek, J.A.; Cox, J.E.; Kohan, D.E. Multiomic identification of factors associated with progression to cystic kidney disease in mice with nephron Ift88 disruption. Am. J. Physiol. Renal. Physiol. 2022, 322, F175–F192. [Google Scholar] [CrossRef]
  94. Lenoir, O.; Tharaux, P.L.; Huber, T.B. Autophagy in kidney disease and aging: Lessons from rodent models. Kidney Int. 2016, 90, 950–964. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Khan, D.; Katoch, A.; Das, A.; Sharathchandra, A.; Lal, R.; Roy, P.; Das, S.; Chattopadhyay, S.; Das, S. Reversible induction of translational isoforms of p53 in glucose deprivation. Cell. Death. Differ. 2015, 22, 1203–1218. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Dent, E.; Wright, O.R.L.; Woo, J.; Hoogendijk, E.O. Malnutrition in older adults. Lancet 2023. [Google Scholar] [CrossRef]
  97. Perhal, A.; Wolf, S.; Jamous, Y.F.; Langer, A.; Abd Alla, J.; Quitterer, U. Increased reactive oxygen species generation contributes to the atherogenic activity of the B2 bradykinin receptor. Front. Med. 2019, 6, 32. [Google Scholar] [CrossRef] [Green Version]
  98. Zha, Y.; Zhuang, W.; Yang, Y.; Zhou, Y.; Li, H.; Liang, J. Senescence in vascular smooth muscle cells and atherosclerosis. Front. Cardiovasc. Med. 2022, 9, 910580. [Google Scholar] [CrossRef]
  99. Yang, Y.; Wang, J.; Shi, F.; Shan, A.; Xu, S.; Lv, W. BDKRB2 is a novel EMT-related biomarker and predicts poor survival in glioma. Aging 2021, 13, 7499–7516. [Google Scholar] [CrossRef]
  100. Kim, M.; Ladomersky, E.; Mozny, A.; Kocherginsky, M.; O’Shea, K.; Reinstein, Z.Z.; Zhai, L.; Bell, A.; Lauing, K.L.; Bollu, L.; et al. Glioblastoma as an age-related neurological disorder in adults. Neurooncol. Adv. 2021, 3, 125. [Google Scholar] [CrossRef]
  101. Shcherbina, L.; Edlund, A.; Esguerra, J.L.; Abels, M.; Zhou, Y.; Ottosson-Laakso, E.; Wollheim, C.B.; Hansson, O.; Eliasson, L.; Wierup, N. Endogenous beta-cell CART regulates insulin secretion and transcription of beta-cell genes. Mol. Cell. Endocrinol. 2017, 447, 52–60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. Aizawa, T. Alteration of insulin secretion in the elderly. Nihon. Rinsho. 2006, 64, 34–38. [Google Scholar] [PubMed]
  103. Abels, M.; Riva, M.; Shcherbina, L.; Fischer, A.T.; Banke, E.; Degerman, E.; Lindqvist, A.; Wierup, N. Overexpressed beta cell CART increases insulin secretion in mouse models of insulin resistance and diabetes. Peptides 2022, 151, 170747. [Google Scholar] [CrossRef] [PubMed]
  104. Shemtov, S.J.; Emani, R.; Bielska, O.; Covarrubias, A.J.; Verdin, E.; Andersen, J.K.; Winer, D.A. The intestinal immune system and gut barrier function in obesity and ageing. FEBS J. 2022. [Google Scholar] [CrossRef]
  105. Lu, J.; Chen, A.; Ma, X.; Shang, X.; Zhang, Y.; Guo, Y.; Liu, M.; Wang, X. Generation and characterization of cytochrome P450 2J3/10 CRISPR/Cas9 knockout rat model. Drug. Metab. Dispos. 2020, 48, 1129–1136. [Google Scholar] [CrossRef]
  106. Evangelista, E.A.; Aliwarga, T.; Sotoodehnia, N.; Jensen, P.N.; McKnight, B.; Lemaitre, R.N.; Totah, R.A.; Gharib, S.A. CYP2J2 modulates diverse transcriptional programs in adult human cardiomyocytes. Sci. Rep. 2020, 10, 5329. [Google Scholar] [CrossRef] [Green Version]
  107. Azam, T.; Zhang, H.; Zhou, F.; Wang, X. Recent advances on drug development and emerging therapeutic agents through targeting cellular homeostasis for ageing and cardiovascular disease. Front. Aging 2022, 3, 888190. [Google Scholar] [CrossRef]
  108. Huang, J.; Zhao, Q.; Li, M.; Duan, Q.; Zhao, Y.; Zhang, H. The effects of endothelium-specific CYP2J2 overexpression on the attenuation of retinal ganglion cell apoptosis in a glaucoma rat model. FASEB J. 2019, 33, 11194–11209. [Google Scholar] [CrossRef] [Green Version]
  109. Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y.; et al. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform. 2016, 54, 1.30.1–1.30.33. [Google Scholar] [CrossRef]
  110. Damour, A.; Garcia, M.; Seneschal, J.; Leveque, N.; Bodet, C. Eczema herpeticum: Clinical and pathophysiological aspects. Clin. Rev. Allergy. Immunol. 2020, 59, 1–18. [Google Scholar] [CrossRef]
  111. Shen, Z.Y.; Liu, W.; Bao, Z.X.; Zhou, Z.T.; Wang, L.Z. Oral melanotic macule and primary oral malignant melanoma: Epidemiology, location involved, and clinical implications. Oral. Surg. Oral. Med. Oral. Pathol. Oral. Radiol. Endod. 2011, 112, e21–e25. [Google Scholar] [CrossRef] [PubMed]
  112. Liu, R.; Zhang, Z.; Liu, H.; Hou, P.; Lang, J.; Wang, S.; Yan, H.; Li, P.; Huang, Z. Human β-defensin 2 is a novel opener of Ca2+-activated potassium channels and induces vasodilation and hypotension in monkeys. Hypertension 2013, 62, 415–425. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Aykut, B.; Ochs, M.; Radhakrishnan, P.; Brill, A.; Hocker, H.; Schwarz, S.; Weissinger, D.; Kehm, R.; Kulu, Y.; Ulrich, A.; et al. EMX2 gene expression predicts liver metastasis and survival in colorectal cancer. BMC Cancer 2017, 17, 555. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  114. Kim, S.E.; Paik, H.Y.; Yoon, H.; Lee, J.E.; Kim, N.; Sung, M.K. Sex- and gender-specific disparities in colorectal cancer risk. World J. Gastroenterol. 2015, 21, 5167–5175. [Google Scholar] [CrossRef]
  115. Feng, Z.; Yin, Y.; Liu, B.; Zheng, Y.; Shi, D.; Zhang, H.; Qin, J. Prognostic and immunological role of FAT family genes in non-small cell lung cancer. Cancer Control. 2022, 29, 10732748221076682. [Google Scholar] [CrossRef]
  116. Hong, H.; Wang, Q.; Li, J.; Liu, H.; Meng, X.; Zhang, H. Aging, cancer and immunity. J. Cancer 2019, 10, 3021–3027. [Google Scholar] [CrossRef] [Green Version]
  117. Willcocks, L.C.; Carr, E.J.; Niederer, H.A.; Rayner, T.F.; Williams, T.N.; Yang, W.; Scott, J.A.; Urban, B.C.; Peshu, N.; Vyse, T.J.; et al. A defunctioning polymorphism in FCGR2B is associated with protection against malaria but susceptibility to systemic lupus erythematosus. Proc. Natl. Acad. Sci. USA 2010, 107, 7881–7885. [Google Scholar] [CrossRef] [Green Version]
  118. Liao, S.; Ning, Q.; Chen, Y.; Zhao, X.; Tang, S. Interaction of aging and immunosenescence: New therapeutic targets of aging. Int. Immunopharmacol. 2022, 113, 109397. [Google Scholar] [CrossRef]
  119. Wang, J.; Chen, S.; Xiao, W.; Li, W.; Wang, L.; Yang, S.; Wang, W.; Xu, L.; Liao, S.; Liu, W.; et al. CAR-T cells targeting CLL-1 as an approach to treat acute myeloid leukemia. J. Hematol. Oncol. 2018, 11, 7. [Google Scholar] [CrossRef] [Green Version]
  120. Mao, Y.; Xu, J.; Xu, X.; Qiu, J.; Hu, Z.; Jiang, F.; Zhou, G. Comprehensive analysis for cellular senescence-related immunogenic characteristics and immunotherapy prediction of acute myeloid leukemia. Front. Pharmacol. 2022, 13, 987398. [Google Scholar] [CrossRef]
  121. Shandilya, J.; Gao, Y.; Nayak, T.K.; Roberts, S.G.; Medler, K.F. AP1 transcription factors are required to maintain the peripheral taste system. Cell Death Dis. 2016, 7, e2433. [Google Scholar] [CrossRef] [Green Version]
  122. Wei, H.; Zhen, T.; Tuo, Y.; Li, H.; Liang, J.; Chen, S.; Shi, H.; Han, A. Clinicopathologic and molecular features of vascular tumors in a series of 118 cases. Am. J. Transl. Res. 2022, 14, 2939–2951. [Google Scholar] [PubMed]
  123. Zhang, W.; He, X.; Yin, H.; Cao, W.; Lin, T.; Chen, W.; Diao, W.; Ding, M.; Hu, H.; Mo, W.; et al. Allosteric activation of the metabolic enzyme GPD1 inhibits bladder cancer growth via the lysoPC-PAFR-TRPV2 axis. J. Hematol. Oncol. 2022, 15, 93. [Google Scholar] [CrossRef] [PubMed]
  124. Richters, A.; Aben, K.K.H.; Kiemeney, L.A.L.M. The global burden of urinary bladder cancer: An update. World. J. Urol. 2020, 38, 1895–1904. [Google Scholar] [CrossRef] [Green Version]
  125. 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. 2020, 48, D731–D742. [Google Scholar] [CrossRef] [PubMed]
  126. Fallahzadeh, M.K.; Borhani Haghighi, A.; Namazi, M.R.; Fallahzadeh, M.H. Beta-thalassemia trait as a protective factor against Alzheimer disease. Alzheimer Dis. Assoc. Disord. 2009, 23, 186–187. [Google Scholar] [CrossRef]
  127. Derakhshani, A.; Safarpour, H.; Abdoli Shadbad, M.; Hemmat, N.; Leone, P.; Asadzadeh, Z.; Pashazadeh, M.; Baradaran, B.; Racanelli, V. The role of hemoglobin subunit delta in the immunopathy of multiple sclerosis: Mitochondria matters. Front. Immunol. 2021, 12, 709173. [Google Scholar] [CrossRef]
  128. Louapre, C.; Papeix, C.; Lubetzki, C.; Maillart, E. Multiple sclerosis and aging. Geriatr. Psychol. Neuropsychiatr. Vieil. 2017, 15, 402–408. [Google Scholar] [CrossRef]
  129. Park, H.K.; Cho, A.R.; Lee, S.C.; Ban, J.Y. MPTP-induced model of Parkinson’s disease in heat shock protein 70.1 knockout mice. Mol. Med. Rep. 2012, 5, 1465–1468. [Google Scholar]
  130. Tatar, M.; Khazaeli, A.A.; Curtsinger, J.W. Chaperoning extended life. Nature 1997, 390, 30. [Google Scholar] [CrossRef]
  131. Singh, R.; Kolvraa, S.; Rattan, S.I. Genetics of human longevity with emphasis on the relevance of HSP70 as candidate genes. Front. Biosci. 2007, 12, 4504–4513. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  132. Li, Y.; Wang, J.; Gao, C.; Hu, Q.; Mao, X. Integral membrane protein 2A enhances sensitivity to chemotherapy via notch signaling pathway in cervical cancer. Bioengineered 2021, 12, 10183–10193. [Google Scholar] [CrossRef] [PubMed]
  133. Smith, A.J.B.; Beavis, A.L.; Rositch, A.F.; Levinson, K. Disparities in diagnosis and treatment of cervical adenocarcinoma compared with squamous cell carcinoma: An analysis of the national cancer database, 2004-2017. J. Low Genit. Tract. Dis. 2023, 27, 29–34. [Google Scholar] [CrossRef] [PubMed]
  134. Zhou, C.; Wang, M.; Yang, J.; Xiong, H.; Wang, Y.; Tang, J. Integral membrane protein 2A inhibits cell growth in human breast cancer via enhancing autophagy induction. Cell. Commun. Signal. 2019, 17, 105. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Kelm-Nelson, C.A.; Gammie, S. Gene expression within the periaqueductal gray is linked to vocal behavior and early-onset parkinsonism in Pink1 knockout rats. BMC Genom. 2020, 21, 625. [Google Scholar] [CrossRef] [PubMed]
  136. Chesnokova, A.Y.; Ekimova, I.V.; Pastukhov, Y.F. Parkinson’s disease and aging. Adv. Gerontol. 2018, 31, 668–678. [Google Scholar]
  137. Cebrat, M.; Strzadala, L.; Kisielow, P. Wnt inhibitory factor-1: A candidate for a new player in tumorigenesis of intestinal epithelial cells. Cancer Lett. 2004, 20, 107–113. [Google Scholar] [CrossRef]
  138. Kim, M.J.; Lee, E.J.; Kim, D.S.; Lee, D.H.; Youk, E.G.; Kim, H.J. Composite intestinal adenoma-microcarcinoid in the colon and rectum: A case series and historical review. Diagn. Pathol. 2017, 12, 78. [Google Scholar] [CrossRef] [Green Version]
  139. 139. Xu, Z.; Gu, Y.; Chen, J.; Chen, X.; Song, Y.; Fan, J.; Ji, X.; Li, Y.; Zhang, W.; Zhang, R. Epigenome-wide gene-age interaction study reveals reversed effects of MORN1 DNA methylation on survival between young and elderly oral squamous cell carcinoma patients. Front. Oncol. 2022, 12, 941731. [Google Scholar] [CrossRef]
  140. Ikawa, H.; Tonogi, M.; Yamane, G.Y.; Yamauchi, T.; Tanaka, Y.; Sato, M.; Matsui, J.; Ando, N.; Katakura, A. Upper gastrointestinal tract cancers as double-cancers in elderly patients with oral squamous cell carcinoma. Bull. Tokyo Dent. Coll. 2012, 53, 9–16. [Google Scholar] [CrossRef] [Green Version]
  141. Banszerus, V.L.; Konig, M.; Landmesser, U.; Vetter, V.M.; Demuth, I. Epigenetic aging in patients diagnosed with coronary artery disease: Results of the LipidCardio study. Clin. Epigenetics 2023, 15, 16. [Google Scholar] [CrossRef] [PubMed]
  142. Kocaman, S.A.; Cetin, M.; Durakoglugil, M.E.; Erdogan, T.; Canga, A.; Cicek, Y.; Dogan, S.; Sahin, I.; Satıroglu, O.; Bostan, M. The degree of premature hair graying as an independent risk marker for coronary artery disease: A predictor of biological age rather than chronological age. Anadolu Kardiyol. Derg. 2012, 12, 457–463. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  143. Rozanski, A.; Takano, A.P.; Kato, P.N.; Soares, A.G.; Lellis-Santos, C.; Campos, J.C.; Ferreira, J.C.; Barreto-Chaves, M.L.; Moriscot, A.S. M-protein is down-regulated in cardiac hypertrophy driven by thyroid hormone in rats. Mol. Endocrinol. 2013, 27, 2055–2065. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  144. Ashton, K.J.; Tupicoff, A.; Williams-Pritchard, G.; Kiessling, C.J.; See Hoe, L.E.; Headrick, J.P.; Peart, J.N. Unique transcriptional profile of sustained ligand-activated preconditioning in pre- and post-ischemic myocardium. PLoS ONE 2013, 8, e72278. [Google Scholar] [CrossRef] [Green Version]
  145. Villanueva, P.; Nudel, R.; Hoischen, A.; Fernandez, M.A.; Simpson, N.H.; Gilissen, C.; Reader, R.H.; Jara, L.; Echeverry, M.M.; Francks, C.; et al. Exome sequencing in an admixed isolated population indicates NFXL1 variants confer a risk for specific language impairment. PLoS Genet. 2015, 11, e1004925. [Google Scholar] [CrossRef] [Green Version]
  146. Martinez-Nicolas, I.; Llorente, T.E.; Martinez-Sanchez, F.; Meilan, J.J.G. Ten years of research on automatic voice and speech analysis of people with alzheimer’s disease and mild cognitive impairment: A systematic review article. Front. Psychol. 2021, 12, 620251. [Google Scholar] [CrossRef]
  147. Park, H.J.; Kim, M.K.; Kim, Y.; Kim, H.J.; Bae, S.K.; Bae, M.K. Neuromedin B modulates phosphate-induced vascular calcification. BMB Rep. 2021, 54, 569–574. [Google Scholar] [CrossRef]
  148. Wu, Y.Y.; Shan, S.K.; Lin, X.; Xu, F.; Zhong, J.Y.; Wu, F.; Duan, J.Y.; Guo, B.; Li, F.X.; Wang, Y.; et al. Cellular crosstalk in the vascular wall microenvironment: The role of exosomes in vascular calcification. Front. Cardiovasc. Med. 2022, 9, 912358. [Google Scholar] [CrossRef]
  149. Sokolov, D.; Sechrest, E.R.; Wang, Y.; Nevin, C.; Du, J.; Kolandaivelu, S. Nuclear NAD+-biosynthetic enzyme NMNAT1 facilitates development and early survival of retinal neurons. Elife 2021, 10, e71185. [Google Scholar] [CrossRef]
  150. Fleckenstein, M.; Keenan, T.D.L.; Guymer, R.H.; Chakravarthy, U.; Schmitz-Valckenberg, S.; Klaver, C.C.; Wong, W.T.; Chew, E.Y. Age-related macular degeneration. Nat. Rev. Dis. Primers 2021, 7, 31. [Google Scholar] [CrossRef]
  151. Zhang, Y.; Guo, X.; Peng, Z.; Liu, C.; Ren, L.; Liang, J.; Wang, P. Nicotinamide mononucleotide adenylyltransferase 1 regulates cerebral ischemia-induced blood-brain barrier disruption through NAD+/SIRT1 signaling pathway. Mol. Neurobiol. 2022, 59, 4879–4891. [Google Scholar] [CrossRef] [PubMed]
  152. Gallego, I.; Villate-Beitia, I.; Saenz-Del-Burgo, L.; Puras, G.; Pedraz, J.L. Therapeutic opportunities and delivery strategies for brain revascularization in stroke, neurodegeneration, and aging. Pharmacol Rev. 2022, 74, 439–461. [Google Scholar] [CrossRef] [PubMed]
  153. Friedrich, B.; Euler, P.; Ziegler, R.; Kuhn, A.; Landwehrmeyer, B.G.; Luthi-Carter, R.; Weiller, C.; Hellwig, S.; Zucker, B. Comparative analyses of Purkinje cell gene expression profiles reveal shared molecular abnormalities in models of different polyglutamine diseases. Brain Res. 2012, 1481, 37–48. [Google Scholar] [CrossRef] [PubMed]
  154. Pradhan, S.; Gao, R.; Bush, K.; Zhang, N.; Wairkar, Y.P.; Sarkar, P.S. Polyglutamine expansion in huntingtin and mechanism of DNA damage repair defects in Huntington’s disease. Front. Cell. Neurosci. 2022, 16, 837576. [Google Scholar] [CrossRef] [PubMed]
  155. Goswami, J.; Martin, L.A.; Goldowitz, D.; Beitz, A.J.; Feddersen, R.M. Enhanced Purkinje cell survival but compromised cerebellar function in targeted anti-apoptotic protein transgenic mice. Mol. Cell. Neurosci. 2005, 29, 202–221. [Google Scholar] [CrossRef]
  156. Chen, T.Y.; Yang, C.Y.; Yang, M.T.; Kuo, T.F.; Chang, C.L.; Chen, C.L.; Lee, T.H.; Yang, G.; Yang, W.C.; Chiu, C.F.; et al. Protein disulfide isomerase a4 promotes lung cancer development via the Stat3 pathway in stromal cells. Clin. Transl. Med. 2022, 12, e606. [Google Scholar] [CrossRef]
  157. Li, H.; Liu, Q.; Xiao, K.; He, Z.; Wu, C.; Sun, J.; Chen, X.; Chen, S.; Yang, J.; Ma, Q.; et al. PDIA4 correlates with poor prognosis and is a potential biomarker in glioma. Oncol. Targets Ther. 2021, 14, 125–138. [Google Scholar] [CrossRef]
  158. Menard, C.; Tse, Y.C.; Cavanagh, C.; Chabot, J.G.; Herzog, H.; Schwarzer, C.; Wong, T.P.; Quirion, R. Knockdown of prodynorphin gene prevents cognitive decline, reduces anxiety, and rescues loss of group 1 metabotropic glutamate receptor function in aging. J. Neurosci. 2013, 33, 12792–12804. [Google Scholar] [CrossRef] [Green Version]
  159. Miki, Y.; Kidoguchi, Y.; Sato, M.; Taketomi, Y.; Taya, C.; Muramatsu, K.; Gelb, M.H.; Yamamoto, K.; Murakami, M. Dual roles of group iid phospholipase A2 in inflammation and cancer. J. Biol. Chem. 2016, 291, 15588–155601. [Google Scholar] [CrossRef] [Green Version]
  160. Ter Haar, E.L.M.; Thomas, S.E.; van den Reek, J.M.P.A.; Otero, M.E.; Njoo, M.D.; Ossenkoppele, P.M.; Kop, E.N.; Dodemont, S.R.P.; Korver, J.E.M.; Kuijpers, A.L.A.; et al. Drug survival, safety, and effectiveness of biologics in older patients with psoriasis: A comparison with younger patients-a BioCAPTURE registry study. Drugs Aging 2022, 39, 715–727. [Google Scholar] [CrossRef]
  161. Tan, J.; Xu, Y.; Han, F.; Ye, X. Genetical modification on adipose-derived stem cells facilitates facial nerve regeneration. Aging 2019, 11, 908–920. [Google Scholar] [CrossRef] [PubMed]
  162. Zhang, Q.; Li, J.; Weng, L. Identification and validation of aging-related genes in Alzheimer’s disease. Front. Neurosci. 2022, 16, 905722. [Google Scholar] [CrossRef] [PubMed]
  163. Cheng, C.W.; Chang, L.C.; Tseng, T.L.; Wu, C.C.; Lin, Y.F.; Chen, J.S. Phosphotriesterase-related protein sensed albuminuria and conferred renal tubular cell activation in membranous nephropathy. J. Biomed. Sci. 2014, 21, 32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  164. Deegens, J.K.; Wetzels, J.F. Membranous nephropathy in the older adult: Epidemiology, diagnosis and management. Drugs Aging 2007, 24, 717–732. [Google Scholar] [CrossRef]
  165. Wu, S.; Zhao, Z.; Liu, S.; Li, M.; Wang, T.; Wang, S.; Xu, M.; Chen, Y.; Dai, M.; Zhang, D.; et al. The association between age at diagnosis of type 2 diabetes and albuminuria in Chinese adults: A nationwide population study. J. Diabetes 2021, 13, 987–997. [Google Scholar] [CrossRef]
  166. Liu, Q.; Li, J.; Zhang, W.; Xiao, C.; Zhang, S.; Nian, C.; Li, J.; Su, D.; Chen, L.; Zhao, Q.; et al. Glycogen accumulation and phase separation drives liver tumor initiation. Cell 2021, 184, 5559–5576.e19. [Google Scholar] [CrossRef]
  167. Gusarov, I.; Nudler, E. Glycogen at the crossroad of stress resistance, energy maintenance, and pathophysiology of aging. Bioessays 2018, 40, e1800033. [Google Scholar] [CrossRef]
  168. Zhao, C.Y.; Hua, C.H.; Li, C.H.; Zheng, R.Z.; Li, X.Y. High PYGL expression predicts poor prognosis in human gliomas. Front. Neurol. 2021, 12, 652931. [Google Scholar] [CrossRef]
  169. Hafez, A.M.; Seleem, M.M.; Alattar, A.Z.; Elshorbagy, S.; Elsayed, W.S.H. RNA-binding proteins RBM-HuR, RBM3 and PODXL expression in urothelial carcinoma of the urinary bladder. prognostic and clinical implications. Contemp. Oncol. 2021, 25, 279–290. [Google Scholar] [CrossRef]
  170. Hamade, A.; Li, D.; Tyryshkin, K.; Xu, M.; Conseil, G.; Yolmo, P.; Hamilton, J.; Chenard, S.; Robert Siemens, D.; Koti, M. Sex differences in the aging murine urinary bladder and influence on the tumor immune microenvironment of a carcinogen-induced model of bladder cancer. Biol. Sex Differ. 2022, 13, 19. [Google Scholar] [CrossRef]
  171. Schupp, M.; Lefterova, M.I.; Janke, J.; Leitner, K.; Cristancho, A.G.; Mullican, S.E.; Qatanani, M.; Szwergold, N.; Steger, D.J.; Curtin, J.C.; et al. Retinol saturase promotes adipogenesis and is downregulated in obesity. Proc. Natl. Acad. Sci. USA 2009, 106, 1105–1110. [Google Scholar] [CrossRef] [Green Version]
  172. Santos, A.L.; Sinha, S. Obesity and aging: Molecular mechanisms and therapeutic approaches. Ageing Res. Rev. 2021, 67, 101268. [Google Scholar] [CrossRef]
  173. Jiang, X.; He, Y.; Shen, Q.; Duan, L.; Yuan, Y.; Tang, L.; Shi, Y.; Liu, B.; Zhai, H.; Shi, P.; et al. RETSAT mutation selected for hypoxia adaptation inhibits tumor growth. Front. Cell Dev. Biol. 2021, 9, 744992. [Google Scholar] [CrossRef]
  174. Kim, K.; Kim, Y.J. RhoBTB3 regulates proliferation and invasion of breast cancer cells via Col1a1. Mol. Cells 2022, 45, 631–639. [Google Scholar] [CrossRef]
  175. Yang, S.H.; Liu, W.; Peng, J.; Xu, Y.J.; Liu, Y.F.; Li, Y.; Peng, M.Y.; Ou-Yang, Z.; Chen, C.; Liu, E.Y. High expression of RhoBTB3 predicts favorable chemothrapy outcomes in non-M3 acute myeloid leukemia. J. Cancer 2021, 12, 4229–4239. [Google Scholar] [CrossRef]
  176. Zjablovskaja, P.; Florian, M.C. Acute myeloid leukemia: Aging and epigenetics. Cancers 2019, 12, 103. [Google Scholar] [CrossRef] [Green Version]
  177. Samuel, C.S.; Unemori, E.N.; Mookerjee, I.; Bathgate, R.A.; Layfield, S.L.; Mak, J.; Tregear, G.W.; Du, X.J. Relaxin modulates cardiac fibroblast proliferation, differentiation, and collagen production and reverses cardiac fibrosis in vivo. Endocrinology 2004, 145, 4125–4133. [Google Scholar] [CrossRef] [Green Version]
  178. Leysen, H.; Walter, D.; Clauwaert, L.; Hellemans, L.; van Gastel, J.; Vasudevan, L.; Martin, B.; Maudsley, S. The relaxin-3 receptor, RXFP3, is a modulator of aging-related disease. Int. J. Mol. Sci. 2022, 23, 4387. [Google Scholar] [CrossRef]
  179. Chou, C.K.; Fan, C.C.; Lin, P.S.; Liao, P.Y.; Tung, J.C.; Hsieh, C.H.; Hung, M.C.; Chen, C.H.; Chang, W.C. Sciellin mediates mesenchymal-to-epithelial transition in colorectal cancer hepatic metastasis. Oncotarget 2016, 7, 25742–25754. [Google Scholar] [CrossRef] [Green Version]
  180. Yamadera, M.; Shinto, E.; Nagata, K.; Shiraishi, T.; Kajiwara, Y.; Mochizuki, S.; Okamoto, K.; Kishi, Y.; Ueno, H. Proposal for a tumor budding predictive score derived from endoscopic biopsy samples in colorectal cancer. Int. J. Clin. Oncol. 2022, 27, 756–764. [Google Scholar] [CrossRef]
  181. Dekker, E.; Tanis, P.J.; Vleugels, J.L.A.; Kasi, P.M.; Wallace, M.B. Colorectal cancer. Lancet. 2019, 394, 1467–1480. [Google Scholar] [CrossRef]
  182. Yang, J.Y.; Deng, X.Y.; Li, Y.S.; Ma, X.C.; Feng, J.X.; Yu, B.; Chen, Y.; Luo, Y.L.; Wang, X.; Chen, M.L.; et al. Structure of Schlafen13 reveals a new class of tRNA/rRNA- targeting RNase engaged in translational control. Nat. Commun. 2018, 9, 1165. [Google Scholar] [CrossRef] [Green Version]
  183. Crouch, J.; Shvedova, M.; Thanapaul, R.J.R.S.; Botchkarev, V.; Roh, D. Epigenetic regulation of cellular senescence. Cells. 2022, 11, 672. [Google Scholar] [CrossRef]
  184. Wang, Q.; Zhou, D.; Wu, F.; Liang, Q.; He, Q.; Peng, M.; Yao, T.; Hu, Y.; Qian, B.; Tang, J.; et al. Immune microenvironment signatures as biomarkers to predict early recurrence of stage ia-b lung cancer. Front. Oncol. 2021, 11, 680287. [Google Scholar] [CrossRef]
  185. Schneider, J.L.; Rowe, J.H.; Garcia-de-Alba, C.; Kim, C.F.; Sharpe, A.H.; Haigis, M.C. The aging lung: Physiology, disease, and immunity. Cell 2021, 184, 1990–2019. [Google Scholar] [CrossRef]
  186. Borowicz, S.; Principe, D.R.; Dorman, M.J.; McHenry, A.J.; Sondarva, G.; Kumar, S.; Ananthanarayanan, V.; Simms, P.E.; Hess, A.; Rana, A. HAI-1 is an independent predictor of lung cancer mortality and is required for M1 macrophage polarization. PLoS ONE 2021, 16, e0252197. [Google Scholar] [CrossRef]
  187. Wu, Q.; Yin, G.; Luo, J.; Zhang, Y.; Ai, T.; Tian, J.; Jin, Y.; Lei, J.; Liu, S. Comprehensive analysis of the expression and prognostic value of SPINT1/2 in breast carcinoma. Front. Endocrinol. 2021, 12, 665666. [Google Scholar] [CrossRef]
  188. Marongiu, F.; DeGregori, J. The sculpting of somatic mutational landscapes by evolutionary forces and their impacts on aging-related disease. Mol. Oncol. 2022, 16, 3238–3258. [Google Scholar] [CrossRef]
  189. Wei, B.; Lu, Y.; Jin, J.P. Deficiency of slow skeletal muscle troponin T causes atrophy of type I slow fibres and decreases tolerance to fatigue. J. Physiol. 2014, 592, 1367–1380. [Google Scholar] [CrossRef]
  190. Angioni, D.; Virecoulon Giudici, K.; Montoya Martinez, M.; Rolland, Y.; Vellas, B.; de Souto Barreto, P. Neuroimaging markers of chronic fatigue in older people: A narrative review. Aging Clin. Exp. Res. 2021, 33, 1487–1492. [Google Scholar] [CrossRef]
  191. Fei, X.; Kong, L.; Shi, C.; Wang, G.; Liu, C.; Wang, C.; Liu, P.; Tan, X. Identification of prognosis-related molecular subgroups and construction of a prognostic prediction model using immune-related genes in pancreatic cancer. J. Oncol. 2022, 2022, 7117014. [Google Scholar] [CrossRef]
  192. Yin, M.Y.; Xi, L.T.; Liu, L.; Zhu, J.Z.; Qian, L.J.; Xu, C.F. Pancreatic cancer incidence and mortality patterns in 2006–2015 and prediction of the epidemiological trend to 2025 in China. World J. Clin. Cases 2022, 10, 4404–4413. [Google Scholar] [CrossRef]
  193. Saul, D.; Geisberg, L.K.; Gehle, T.; Hoffmann, D.B.; Tezval, M.; Sehmisch, S.; Komrakova, M. Changes in musculoskeletal system and metabolism in osteoporotic rats treated with urocortin. Front. Endocrinol. 2019, 10, 400. [Google Scholar] [CrossRef]
  194. Latchman, D.S. Urocortin. Int. J. Biochem. Cell Biol. 2002, 34, 907–910. [Google Scholar] [CrossRef]
  195. Laurence, H.; Kumar, S.; Owston, M.A.; Lanford, R.E.; Hubbard, G.B.; Dick, E.J., Jr. Natural mortality and cause of death analysis of the captive chimpanzee (Pan troglodytes): A 35-year review. J. Med. Primatol. 2017, 46, 106–115. [Google Scholar] [CrossRef]
  196. Che, J.; Jin, Z.; Yan, F.; You, J.; Xie, J.; Chen, B.; Cheng, G.; Zhu, H.; He, Q.; Hu, Y.; et al. Discovery of 5,6-Bis(4-methoxy-3-methylphenyl)pyridin-2-amine as a WSB1 degrader to inhibit cancer cell metastasis. J. Med. Chem. 2021, 64, 8621–8643. [Google Scholar] [CrossRef]
  197. Fane, M.; Weeraratna, A.T. Normal aging and its role in cancer metastasis. Cold Spring Harb. Perspect. Med. 2020, 10, a037341. [Google Scholar] [CrossRef]
  198. Kim, J.J.; Lee, S.B.; Yi, S.Y.; Han, S.A.; Kim, S.H.; Lee, J.M.; Tong, S.Y.; Yin, P.; Gao, B.; Zhang, J.; et al. WSB1 overcomes oncogene-induced senescence by targeting ATM for degradation. Cell Res. 2017, 27, 274–293. [Google Scholar] [CrossRef] [Green Version]
  199. Wang, J.; Li, Z.; Xu, L.; Yang, H.; Liu, W. Transmembrane domain dependent inhibitory function of FcγRIIB. Protein Cell 2018, 9, 1004–1012. [Google Scholar] [CrossRef]
  200. Morand, S.; McIntyre, K.M.; Baylis, M. Domesticated animals and human infectious diseases of zoonotic origins: Domestication time matters. Infect. Genet. Evol. 2014, 24, 76–81. [Google Scholar] [CrossRef] [Green Version]
  201. Zhang, X.; Jamwal, K.; Distl, O. Tracking footprints of artificial and natural selection signatures in breeding and non-breeding cats. Sci. Rep. 2022, 12, 18061. [Google Scholar] [CrossRef]
  202. Theofanopoulou, C.; Gastaldon, S.; O’Rourke, T.; Samuels, B.D.; 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]
  203. 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]
  204. 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. [Google Scholar] [CrossRef]
  205. Albert, F.W.; Somel, M.; Carneiro, M.; Aximu-Petri, A.; Halbwax, M.; Thalmann, O.; Blanco-Aguiar, J.A.; 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] [Green Version]
  206. Sato, D.X.; Rafati, N.; Ring, H.; Younis, S.; Feng, C.; Blanco-Aguiar, J.A.; Rubin, C.J.; Villafuerte, R.; Hallbook, 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]
  207. 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]
  208. Hekman, J.P.; Johnson, J.L.; 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. Genes Genomes Genet. 2018, 8, 859–873. [Google Scholar] [CrossRef] [Green Version]
  209. 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]
  210. 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]
  211. Fallahshahroudi, A.; Lotvedt, P.; Belteky, J.; Altimiras, J.; Jensen, P. Changes in pituitary gene expression may underlie multiple domesticated traits in chickens. Hered. Edinb. 2019, 122, 195–204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  212. Samet, H. A top-down quadtree traversal algorithm. IEEE. Trans. Pattern. Anal. Mach. Intell. 1985, 7, 94–98. [Google Scholar] [CrossRef] [PubMed]
  213. 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]
  214. 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]
  215. 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]
  216. 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. Phylogenetics Evol. 2021, 163, 107257. [Google Scholar] [CrossRef]
  217. Clarke, L.E.; Liddelow, S.A.; Chakraborty, C.; Munch, A.E.; Heiman, M.; Barres, B.A. Normal aging induces A1-like astrocyte reactivity. Proc. Natl. Acad. Sci. USA 2018, 115, E1896–E1905. [Google Scholar] [CrossRef] [Green Version]
  218. Day, I.N. dbSNP in the detail and copy number complexities. Hum. Mutat. 2010, 31, 2–4. [Google Scholar] [CrossRef]
  219. Ponomarenko, M.; Rasskazov, D.; Arkova, O.; Ponomarenko, P.; Suslov, V.; Savinkova, L.; Kolchanov, N. How to use SNP_TATA_Comparator to find a significant change in gene expression caused by the regulatory SNP of this gene’s promoter via a change in affinity of the TATA-binding protein for this promoter. Biomed. Res. Int. 2015, 2015, 359835. [Google Scholar] [CrossRef] [Green Version]
  220. Rasskazov, D.; Chadaeva, I.; Sharypova, E.; Zolotareva, K.; Khandaev, B.; Ponomarenko, P.; Podkolodnyy, N.; Tverdokhleb, N.; Vishnevsky, O.; Bogomolov, A.; et al. Plant_SNP_TATA_Z-Tester: A Web Service that unequivocally estimates the impact of proximal promoter mutations on plant gene expression. Int. J. Mol. Sci. 2022, 23, 8684. [Google Scholar] [CrossRef]
  221. Korkuc, P.; Schippers, J.H.; Walther, D. Characterization and identification of cis-regulatory elements in Arabidopsis based on single-nucleotide polymorphism information. Plant Physiol. 2014, 164, 181–200. [Google Scholar] [CrossRef] [Green Version]
  222. 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]
  223. 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]
  224. Ponomarenko, M.P.; Chadaeva, I.V.; Ponomarenko, P.M.; Bogomolov, A.G.; Oshchepkov, D.Y.; Sharypova, E.B.; Suslov, V.V.; Osadchuk, A.V.; Osadchuk, L.V.; Matushkin, Y.G. A bioinformatic search for correspondence between differentially expressed genes of domestic versus wild animals and orthologous human genes altering reproductive potential. Vavilovskii Zhurnal Genet. Selektsii 2022, 26, 96–108. [Google Scholar] [CrossRef]
  225. Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; et al. NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 2013, 41, D991–D995. [Google Scholar] [CrossRef] [Green Version]
  226. Ye, Q.; Huang, Z.; Lu, W.; Yan, F.; Zeng, W.; Xie, J.; Zhong, W. Identification of the common differentially expressed genes and pathogenesis between neuropathic pain and aging. Front. Neurosci. 2022, 16, 994575. [Google Scholar] [CrossRef]
  227. Ivanisenko, V.A.; Demenkov, P.S.; Ivanisenko, T.V.; Mishchenko, E.L.; Saik, O.V. A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics. 2019, 20, 34. [Google Scholar] [CrossRef]
  228. Rappaport, N.; Twik, M.; Plaschkes, I.; Nudel, R.; Iny Stein, T.; Levitt, J.; Gershoni, M.; Morrey, C.P.; Safran, M.; Lancet, D. MalaCards: An amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 2017, 45, D877–D887. [Google Scholar] [CrossRef] [Green Version]
  229. Mustafin, Z.S.; Lashin, S.A.; Matushkin, Y.G.; Gunbin, K.V.; Afonnikov, D.A. Orthoscape: A cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles. BMC Bioinformatics 2017, 18, 1427. [Google Scholar] [CrossRef] [Green Version]
  230. Mustafin, Z.S.; Zamyatin, V.I.; Konstantinov, D.K.; Doroshkov, A.V.; Lashin, S.A.; Afonnikov, D.A. Phylostratigraphic analysis shows the earliest origination of the abiotic stress associated genes in A. thaliana. Genes 2019, 10, 963. [Google Scholar] [CrossRef] [Green Version]
  231. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef] [PubMed]
  232. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. [Google Scholar] [CrossRef] [PubMed]
  233. Guilliams, M.; Bruhns, P.; Saeys, Y.; Hammad, H.; Lambrecht, B.N. The function of Fcγ receptors in dendritic cells and macrophages. Nat. Rev. Immunol. 2014, 14, 94–108. [Google Scholar] [CrossRef] [PubMed]
  234. Okabe, Y.; Medzhitov, R. Tissue biology perspective on macrophages. Nat. Immunol. 2016, 17, 9–17. [Google Scholar] [CrossRef] [PubMed]
  235. Safran, M.; Rosen, N.; Twik, M.; BarShir, R.; Iny Stein, T.; Dahary, D.; Fishilevich, S.; Lancet, D. The GeneCards Suite. In Practical Guide to Life Science Databases; Abugessaisa, I., Kasukawa, T., Eds.; Springer: Singapore, 2022; pp. 27–56. [Google Scholar] [CrossRef]
  236. Zhang, L.; Pitcher, L.E.; Yousefzadeh, M.J.; Niedernhofer, L.J.; Robbins, P.D.; Zhu, Y. Cellular senescence: A key therapeutic target in aging and diseases. J. Clin. Investig. 2022, 132, e158450. [Google Scholar] [CrossRef] [PubMed]
  237. Bleve, A.; Motta, F.; Durante, B.; Pandolfo, C.; Selmi, C.; Sica, A. Immunosenescence, inflammaging, and frailty: Role of myeloid cells in age-related diseases. Clin. Rev. Allergy Immunol. 2022, 22, e8909–e9107. [Google Scholar] [CrossRef]
  238. 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]
  239. Paxinos, G.; Watson, C.R. The Rat Brain in Stereotaxic Coordinates, 7th ed.; Academic Press: London, UK, 2013; p. 472. [Google Scholar]
  240. 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]
  241. 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. Genom. Biol. 2013, 14, 36. [Google Scholar] [CrossRef] [Green Version]
  242. 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. Bioinform. 2009, 25, 2078–2079. [Google Scholar] [CrossRef] [Green Version]
  243. 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] [PubMed] [Green Version]
  244. 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]
  245. Anders, S.; Huber, W. Differential expression analysis for sequence count data. Genom. Biol. 2010, 11, 106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  246. 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] [Green Version]
  247. Zerbino, D.; Wilder, S.; Johnson, N.; Juettemann, T.; Flicek, P. The Ensembl regulatory build. Genom. Biol. 2015, 16, 56. [Google Scholar] [CrossRef] [Green Version]
  248. Haeussler, M.; Raney, B.; Hinrichs, A.; Clawson, H.; Zweig, A.; Karolchik, D.; Casper, J.; Speir, M.; Haussler, D.; Kent, W. Navigating protected genomics data with UCSC Genome Browser in a box. Bioinformatics 2015, 31, 764–766. [Google Scholar] [CrossRef] [Green Version]
  249. Stajich, J.E.; Block, D.; Boulez, K.; Brenner, S.E.; Chervitz, S.A.; Dagdigian, C.; Fuellen, G.; Gilbert, J.G.; Korf, I.; Lapp, H.; et al. The Bioperl toolkit: Perl modules for the life sciences. Genom. Res. 2002, 12, 1611–1618. [Google Scholar] [CrossRef] [Green Version]
  250. Hahn, S.; Buratowski, S.; Sharp, P.; Guarente, L. Yeast TATA-binding protein TFIID binds to TATA elements with both consensus and nonconsensus DNA sequences. Proc. Natl. Acad. Sci. USA 1989, 86, 5718–5722. [Google Scholar] [CrossRef] [Green Version]
  251. Ponomarenko, P.; Savinkova, L.; Drachkova, I.; Lysova, M.; Arshinova, T.; Ponomarenko, M.; Kolchanov, N. A step-by-step model of TBP/TATA box binding allows predicting human hereditary diseases by single nucleotide polymorphism. Dokl. Biochem. Biophys. 2008, 419, 88–92. [Google Scholar] [CrossRef]
  252. Bucher, P. Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. J. Mol. Biol. 1990, 212, 563–578. [Google Scholar] [CrossRef]
  253. Karas, H.; Knuppel, R.; Schulz, W.; Sklenar, H.; Wingender, E. Combining structural analysis of DNA with search routines for the detection of transcription regulatory elements. Comput. Appli. Biosci. 1996, 12, 441–446. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  254. Ponomarenko, M.; Ponomarenko, J.; Frolov, A.; Podkolodny, N.; Savinkova, L.; Kolchanov, N.; Overton, G. Identification of sequence-dependent features correlating to activity of DNA sites interacting with proteins. Bioinformatics 1999, 15, 687–703. [Google Scholar] [CrossRef] [Green Version]
  255. Waardenberg, A.; Basset, S.; Bouveret, R.; Harvey, R. CompGO: An R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments. BMC Bioinform. 2015, 16, 275. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  256. Varzari, A.; Tudor, E.; Bodrug, N.; Corloteanu, A.; Axentii, E.; Deyneko, I.V. Age-specific association of CCL5 gene polymorphism with pulmonary tuberculosis: A case-control study. Genet. Test. Mol. Biomarkers 2018, 22, 281–287. [Google Scholar] [CrossRef] [PubMed]
  257. Coleman, R.A.; Pugh, B.F. Evidence for functional binding and stable sliding of the TATA binding protein on nonspecific DNA. J. Biol. Chem. 1995, 270, 13850–13859. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  258. Berg, O.G.; von Hippel, P.H. Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. J. Mol. Biol. 1987, 193, 723–750. [Google Scholar] [CrossRef] [PubMed]
  259. Flatters, D.; Lavery, R. Sequence-dependent dynamics of TATA-box binding sites. Biophys. J. 1998, 75, 372–381. [Google Scholar] [CrossRef] [Green Version]
  260. Kim, J.L.; Nikolov, D.B.; Burley, S.K. Co-crystal structure of TBP recognizing the minor groove of a TATA element. Nature 1993, 365, 520–527. [Google Scholar] [CrossRef]
  261. Kim, Y.; Geiger, J.H.; Hahn, S.; Sigler, P.B. Crystal structure of a yeast TBP/TATA-box complex. Nature 1993, 365, 512–520. [Google Scholar] [CrossRef]
  262. Delgadillo, R.F.; Whittington, J.E.; Parkhurst, L.K.; Parkhurst, L.J. The TATA-binding protein core domain in solution variably bends TATA sequences via a three-step binding mechanism. Biochemistry 2009, 48, 1801–1809. [Google Scholar] [CrossRef]
Ijms 24 03996 g001 550
Figure 1. qPCR-based selective verification of the DEGs identified with RNA-Seq in the PAG of tame versus aggressive rats. Legend: (a) Both DEGs, Ascl3 and Defb17, are significantly overexpressed in the PAG of the tame adult male rats (white bars) compared to their aggressive conspecifics (gray bars). Bar height: mean; error bars: standard error of the mean [SEM]; the double asterisk (i.e., double character “**”) denotes statistical significance at p < 0.01 according to nonparametric Mann–Whitney U test and parametric Fisher’s Z-test. (b) Pearson’s linear correlation between the relative expression levels of Ascl3, Defb17, and the reference genes [B2m (β-2-microglobulin) and Rpl30 (ribosomal protein L30) appearing as circles on the plot] in the tame versus aggressive rats is statistically significant, whether measured experimentally using RNA-Seq (x-axis) or qPCR (y-axis) and expressed on the log2 scale (see “Materials and Methods”). Dashed and dot-dash lines denote linear regression and its 95% confidence interval boundaries calculated using STATISTICA (StatsoftTM, Tulsa, OK, USA); r and p are Pearson’s linear correlation coefficient and its statistical significance, respectively.
Figure 1. qPCR-based selective verification of the DEGs identified with RNA-Seq in the PAG of tame versus aggressive rats. Legend: (a) Both DEGs, Ascl3 and Defb17, are significantly overexpressed in the PAG of the tame adult male rats (white bars) compared to their aggressive conspecifics (gray bars). Bar height: mean; error bars: standard error of the mean [SEM]; the double asterisk (i.e., double character “**”) denotes statistical significance at p < 0.01 according to nonparametric Mann–Whitney U test and parametric Fisher’s Z-test. (b) Pearson’s linear correlation between the relative expression levels of Ascl3, Defb17, and the reference genes [B2m (β-2-microglobulin) and Rpl30 (ribosomal protein L30) appearing as circles on the plot] in the tame versus aggressive rats is statistically significant, whether measured experimentally using RNA-Seq (x-axis) or qPCR (y-axis) and expressed on the log2 scale (see “Materials and Methods”). Dashed and dot-dash lines denote linear regression and its 95% confidence interval boundaries calculated using STATISTICA (StatsoftTM, Tulsa, OK, USA); r and p are Pearson’s linear correlation coefficient and its statistical significance, respectively.
Ijms 24 03996 g001
Ijms 24 03996 g002 550
Figure 2. Comparison of the novel DEGs in the PAG of the tame and aggressive rats with the known DEGs related to animal ARD susceptibility and resistance. Legend: See the legend to Figure 1 and the footnote to Table 4; the x- and y-axes correspond to columns iii and x of Table S1; circles correspond to rows of Table S1; R, τ, and γ are the coefficients of Spearman’s rank correlation, Kendall’s rank correlation, and the Goodman–Kruskal generalized correlation, respectively, calculated using STATISTICA (StatsoftTM, Tulsa, OK, USA); PC1 and PC2: principal components calculated in the bootstrap-based refinement mode of the PAST4.04 software [85].
Figure 2. Comparison of the novel DEGs in the PAG of the tame and aggressive rats with the known DEGs related to animal ARD susceptibility and resistance. Legend: See the legend to Figure 1 and the footnote to Table 4; the x- and y-axes correspond to columns iii and x of Table S1; circles correspond to rows of Table S1; R, τ, and γ are the coefficients of Spearman’s rank correlation, Kendall’s rank correlation, and the Goodman–Kruskal generalized correlation, respectively, calculated using STATISTICA (StatsoftTM, Tulsa, OK, USA); PC1 and PC2: principal components calculated in the bootstrap-based refinement mode of the PAST4.04 software [85].
Ijms 24 03996 g002
Ijms 24 03996 g003 550
Figure 3. Animal ARD-linked DEGs are relevant to humans. Legend: See the legend to Figure 2 and the footnote to Table 2.
Figure 3. Animal ARD-linked DEGs are relevant to humans. Legend: See the legend to Figure 2 and the footnote to Table 2.
Ijms 24 03996 g003
Ijms 24 03996 g004 550
Figure 4. An RNA-Seq experiment run within the mouse model of human ARDs [217] revealed a statistically significant increase in Fcgr2b expression in astrocytes from the hippocampus (open circles, ○), striatum (gray circles, ●) and cortex (black circles, ●) with increasing age, providing independent in vivo evidence in support of this finding. Legend: See the legend to Figure 1 and Figure 2; FPKM, fragments per kilobase of transcript per million mapped reads [217].
Figure 4. An RNA-Seq experiment run within the mouse model of human ARDs [217] revealed a statistically significant increase in Fcgr2b expression in astrocytes from the hippocampus (open circles, ○), striatum (gray circles, ●) and cortex (black circles, ●) with increasing age, providing independent in vivo evidence in support of this finding. Legend: See the legend to Figure 1 and Figure 2; FPKM, fragments per kilobase of transcript per million mapped reads [217].
Ijms 24 03996 g004
Ijms 24 03996 g005 550
Figure 5. The FCGR2B-related associative gene network as a data-mining summary of molecular genetic research articles and databases. The network was built using the automated mode of our publicly available web service ANDSystem [227] with “Human, FCGR2B, gene, protein” as input. Legend: Nodes: red circles, proteins; icons “double DNA helix”, genes; “Bowl of Hygieia” symbols, diseases. Edges: turquoise sharp and blunt arrows, expression and degradation, respectively; pink sharp and blunt arrows, up- and downregulation, respectively; blue arrows, transport regulation; purple, orange, and black lines: involvement, coexpression, and association, respectively. CRP and APCS, pentraxin 1 and 2, respectively; CD19, CD36, CD40, and CD79A, proteins CD19, CD36, CD40, and CD79a, respectively; CDK11B, cyclin-dependent kinase 11B; CRP, C-reactive protein; FAS, Fas cell surface death receptor; FASN, fatty acid synthase; FCGR1A, FCGR2A, FCGR2C, FCGR3A, and FCGR3B, Fcγ receptors Ia, IIa, IIc, IIIa, and IIIb, respectively; FOS, FOSB, JUN, JUNB, and JUND, transcription factor AP-1 subunits Fos, FosB, Jun, JunB, and JunD, respectively; GATA4, GATA-binding protein 4; GRASP65, Golgi reassembly-stacking protein 1; HDAC9, histone deacetylase 9; IFNG, interferon γ; IL3, IL4, IL10, and IL13, interleukins 3, 4, 10, and 13, respectively; INPP5D, inositol polyphosphate-5-phosphatase D; INS, insulin; ITGAM, integrin subunit αM; KARS1, lysyl-tRNA synthetase 1; KRT20, keratin 20; LGALS3, galectin 3; LYN, tyrosine-protein kinase Lyn; MS4A1, membrane-spanning 4-domains subfamily A member 1; PTPN11, protein tyrosine phosphatase non-receptor type 11; RELA, NFκB subunit Rela; SH2D1A, SH2 domain-containing protein 1A; SMAD3, SMAD family member 3; STAT3, signal transducer and activator of transcription 3; SYK, spleen tyrosine kinase; SYT1, synaptotagmin 1; TGFB1 and TGFBR2, transforming growth factor β1 and its receptor II TLR9 (Toll-like receptor 9); VWF, von Willebrand factor; WNK1, WNK lysine-deficient protein kinase 1.
Figure 5. The FCGR2B-related associative gene network as a data-mining summary of molecular genetic research articles and databases. The network was built using the automated mode of our publicly available web service ANDSystem [227] with “Human, FCGR2B, gene, protein” as input. Legend: Nodes: red circles, proteins; icons “double DNA helix”, genes; “Bowl of Hygieia” symbols, diseases. Edges: turquoise sharp and blunt arrows, expression and degradation, respectively; pink sharp and blunt arrows, up- and downregulation, respectively; blue arrows, transport regulation; purple, orange, and black lines: involvement, coexpression, and association, respectively. CRP and APCS, pentraxin 1 and 2, respectively; CD19, CD36, CD40, and CD79A, proteins CD19, CD36, CD40, and CD79a, respectively; CDK11B, cyclin-dependent kinase 11B; CRP, C-reactive protein; FAS, Fas cell surface death receptor; FASN, fatty acid synthase; FCGR1A, FCGR2A, FCGR2C, FCGR3A, and FCGR3B, Fcγ receptors Ia, IIa, IIc, IIIa, and IIIb, respectively; FOS, FOSB, JUN, JUNB, and JUND, transcription factor AP-1 subunits Fos, FosB, Jun, JunB, and JunD, respectively; GATA4, GATA-binding protein 4; GRASP65, Golgi reassembly-stacking protein 1; HDAC9, histone deacetylase 9; IFNG, interferon γ; IL3, IL4, IL10, and IL13, interleukins 3, 4, 10, and 13, respectively; INPP5D, inositol polyphosphate-5-phosphatase D; INS, insulin; ITGAM, integrin subunit αM; KARS1, lysyl-tRNA synthetase 1; KRT20, keratin 20; LGALS3, galectin 3; LYN, tyrosine-protein kinase Lyn; MS4A1, membrane-spanning 4-domains subfamily A member 1; PTPN11, protein tyrosine phosphatase non-receptor type 11; RELA, NFκB subunit Rela; SH2D1A, SH2 domain-containing protein 1A; SMAD3, SMAD family member 3; STAT3, signal transducer and activator of transcription 3; SYK, spleen tyrosine kinase; SYT1, synaptotagmin 1; TGFB1 and TGFBR2, transforming growth factor β1 and its receptor II TLR9 (Toll-like receptor 9); VWF, von Willebrand factor; WNK1, WNK lysine-deficient protein kinase 1.
Ijms 24 03996 g005
Ijms 24 03996 g006 550
Figure 6. A comparison between 48 human FCGR2B-associated genes using the publicly available toolbox ANDSystem [227] (Figure 5) and the human genes homologous to 39 novel PAG-related rat DEGs (Table 2). Legend: the double asterisk (i.e., double character “**”) denotes statistical significance at p < 0.01 according to nonparametric Mann–Whitney U test and parametric Fisher’s Z-test; PAI, a gene’s phylostratigraphic age index evaluated against the BLAST-based scale [232] using the freely available web service Orthoscape [229]; BLAST-based PAI scale: 0, Cellular organisms; 1, Eukaryota; 2, Opisthokonta; 3, Metazoa; 4, Eumetazoa; 5, Bilateria; 6, Deuterostomia; 7, Chordata; 8, Craniata; 9, Vertebrata; 10, Gnathostomata; 11, Teleostomi; 12, Euteleostomi; 13, Sarcopterygii; 14, Dipnotetrapodomorpha; 15, Tetrapoda; 16, Amniota; 17, Mammalia; 18, Theria; 19, Eutheria; 20, Euarchontoglires; 21, Primates; 22, Haplorrhini; 23, Simiiformes; 24, Catarrhini; 25, Hominoidea; 26, Hominidae; 27, Homininae; and 28, Homo.
Figure 6. A comparison between 48 human FCGR2B-associated genes using the publicly available toolbox ANDSystem [227] (Figure 5) and the human genes homologous to 39 novel PAG-related rat DEGs (Table 2). Legend: the double asterisk (i.e., double character “**”) denotes statistical significance at p < 0.01 according to nonparametric Mann–Whitney U test and parametric Fisher’s Z-test; PAI, a gene’s phylostratigraphic age index evaluated against the BLAST-based scale [232] using the freely available web service Orthoscape [229]; BLAST-based PAI scale: 0, Cellular organisms; 1, Eukaryota; 2, Opisthokonta; 3, Metazoa; 4, Eumetazoa; 5, Bilateria; 6, Deuterostomia; 7, Chordata; 8, Craniata; 9, Vertebrata; 10, Gnathostomata; 11, Teleostomi; 12, Euteleostomi; 13, Sarcopterygii; 14, Dipnotetrapodomorpha; 15, Tetrapoda; 16, Amniota; 17, Mammalia; 18, Theria; 19, Eutheria; 20, Euarchontoglires; 21, Primates; 22, Haplorrhini; 23, Simiiformes; 24, Catarrhini; 25, Hominoidea; 26, Hominidae; 27, Homininae; and 28, Homo.
Ijms 24 03996 g006
Table
Table 1. A summary of searches for DEGs in the PAG transcriptomes of three tame adult male rats (R. norvegicus) and three aggressive adult male rats, all animals being unrelated.
Table 1. A summary of searches for DEGs in the PAG transcriptomes of three tame adult male rats (R. norvegicus) and three aggressive adult male rats, all animals being unrelated.
GroupTame vs. Aggressive Rats
Total number of sequence reads (NCBI SRA ID: PRJNA668014)210,128,758
Reads mapped to reference rat genome RGSC Rnor_6.0, UCSC Rn6, July 2014 (%)177,608,837 (84.5%)
Expressed genes identified14,039
Statistically significant DEGs (PADJ < 0.05, Fisher’s Z-test with the Benjamini correction)39
Table
Table 2. Statistically significant DEGs newly identified in the PAG of the tame and aggressive adult male rats.
Table 2. Statistically significant DEGs newly identified in the PAG of the tame and aggressive adult male rats.
#Rat Gene NameSymbollog2PADJ
1Amylase α1AAmy1a1.97<0.05
2Aldehyde oxidase 1Aox11.82<0.05
3Achaete-scute family bHLH transcription factor 3Ascl32.74<10−4
4BTG3-associated nuclear proteinBanp−0.64<0.05
5Bradykinin receptor B2Bdkrb21.62<0.05
6Cocaine- and amphetamine-regulated transcript prepropeptideCartpt3.53<10−7
7Cytochrome P450, family 2, subfamily j, polypeptide 10Cyp2j101.10<10−2
8Defensin β17Defb176.96<10−2
9Empty spiracles homeobox 2Emx21.45<10−2
10FAT atypical cadherin 2Fat2−4.96<0.05
11Fc γ receptor IIbFcgr2b2.02<0.05
12AP-1 transcription factor subunit FosB proto-oncogeneFosb−1.85<10−3
13Glycerol-3-phosphate dehydrogenase 1Gpd1−0.99<0.05
14Hemoglobin, β adult major chainHbb-b1−5.90<10−5
15Heat shock protein family A (Hsp70) member 1BHspa1b−2.14<10−7
16Integral membrane protein 2AItm2a0.76<10−2
17Keratin 2Krt2−1.85<10−6
18MORN repeat-containing 1Morn10.75<0.05
19Myomesin 2Myom2−1.63<0.05
20Nuclear transcription X-box-binding like 1 factorNfxl10.69<10−2
21Neuromedin BNmb−3.27<10−4
22Nicotinamide nucleotide adenylyltransferase 1Nmnat10.94<10−4
23Purkinje cell protein 2Pcp2−5.91<10−2
24Protein disulfide isomerase family A member 4Pdia4−0.59<10−2
25ProdynorphinPdyn−1.02<10−2
26Phospholipase A2, group IICPla2g2c−1.60<10−4
27Procollagen-lysine,2-oxoglutarate 5-dioxygenase 1Plod1−0.85<10−2
28Phosphotriesterase-related proteinPter1.27<10−2
29Glycogen phosphorylase LPygl−0.95<0.05
30RNA-binding motif protein 3Rbm31.22<10−4
31Retinol saturaseRetsat−1.01<0.05
32Rho-related BTB domain-containing protein 3Rhobtb30.85<0.05
33Relaxin 3Rln3−3.73<10−3
34SciellinScel1.24<0.05
35Schlafen family member 13Slfn131.33<0.05
36Serine peptidase inhibitor, Kunitz type 1Spint1−1.16<10−3
37Troponin T type 1 (skeletal, slow)Tnnt12.80<0.05
38UrocortinUcn2.61<0.05
39WD repeat and SOCS box-containing protein 1Wsb10.95<0.05
Note: Hereinafter, log2: the log2-transformed fold change (i.e., the ratio of the expression level of a given gene in tame rats to that in aggressive rats); p and PADJ: statistical significance according to Fisher’s Z-test and the Benjamini correction for multiple comparisons.
Table
Table 3. qPCR data on a selection of DEGs from the PAG of 16 additional unrelated adult male rats, eight tame and eight aggressive.
Table 3. qPCR data on a selection of DEGs from the PAG of 16 additional unrelated adult male rats, eight tame and eight aggressive.
DesignGlove Test [79] and qPCR Data on Gene Expression [This Work]
RatSetNo. 12345678
Glove
test		A−3.5−3.5−3.5−3.5−3.5−3.5−3.5−3.5
T3.53.53.53.53.53.53.53.5
DEGSetRelative expression with respect to three reference genes, qPCR, M0 ± SEMTOTAL
Ascl3A0.10 ± 0.050.26 ± 0.010.64 ± 01380.12 ± 0.030.24 ± 0.070.10 ± 0.030.13 ± 0.0050.15 ± 0.020.22 ± 0.06
T1.63 ± 0.031.66 ± 0.161.86 ± 0.154.30 ± 0.063.37 ± 0.184.91 ± 0.134.14 ± 0.904.95 ± 0.863.35 ± 0.51
Defb17A0.007 ± 0.005NDNDND0.005 ± 0.0050.005 ± 0.0050.005 ± 0.0050.005 ± 0.0050.005 ± 0.005
T3.17 ± 0.303.06 ± 0.162.91 ± 0.112.74 ± 0.221.62 ± 0.173.42 ± 0.511.67 ± 0.073.77 ± 0.072.08 ± 0.27
Note: Datasets: A, aggressive rats; T, tame rats. qPCR data: “M0 ± SEM” denotes the mean ± standard error of the mean for three technical replicates for each rat; ND, not detected.
Table
Table 4. Found in PubMed [75]: DEGs in human ARD models based on animal data.
Table 4. Found in PubMed [75]: DEGs in human ARD models based on animal data.
#SpeciesARD SusceptibilityARD ResistanceTissueNDEGRef.
1ratOXYS: spurt aging, 20-day-oldWistar, 20-day-oldhippocampus46[44]
2ratOXYS: spurt aging, 5-month-oldWistar, 5-month-oldhippocampus28[44]
3ratOXYS: spurt aging, 18-month-oldWistar, 18-month-oldhippocampus85[44]
4ratOXYS: spurt aging, 18-month-oldWistar, 18-month-oldprefrontal cortex59[45]
5ratOXYS: spurt aging, 18-month-oldWistar, 18-month-oldretina77[46]
6ratISIAH (hypertensive aged vessels)WAG (norm)brain stem206[47]
7ratISIAH (hypertensive aged vessels)WAG (norm)hypothalamus137[48]
8ratISIAH (hypertensive aged vessels)WAG (norm)renal medulla882[49]
9ratISIAH (hypertensive aged vessels)WAG (norm)renal cortex309[50]
10ratISIAH (hypertensive aged vessels)WAG (norm)adrenal gland1020[51]
11ratSHR (hypertensive aged vessels)Wistar (norm)brain pericytes21[52]
12rat20-passage-old5-passage-oldMSC(BM)9167[35]
13rat5-passage-old5-passage-old, AspirinMSC(BM)1220[35]
14rat20-passage-old20-passage-old, AspirinMSC(BM)446[35]
15mice11-month-old, bone fragility2-month-old, normbone1011[55]
16mice23-month-old, bone fragility2-month-old, normbone1151[55]
17mice30-month-old, bone fragility2-month-old, normbone3725[55]
18mice30-month-old, bone fragility2-month-old, normkidney43[56]
19mice27-month-old, males, renal fibrosis2-month-old, maleskidney349[56]
20mice27-month-old, females, renal fibrosis2-month-old, femaleskidney100[56]
21mice24-month-old, renal fibrosis3-month-oldkidney599[57]
22micePolG D257A, cardiac disorderwild-typed normheart right ventricle402[58]
23mice20-month-old, parabiont, 8 weeks6-month-old, parabiont, 8 weeksaortic arch23[59]
24mice8 h:8 h biorhythm (autistic-like)12 h/12 h biorhythm normhippocampus158[60]
25micewild-type, 20-week-old, 60% dietwild-type, 20-week-old, ad libitumskeletal muscle1178[61]
26micewild-type, 80-week-old, 60% dietwild-type, 80-week-old, ad libitumskeletal muscle747[61]
27miceSirt1-KO, 20-week-old, 60% dietSirt1-KO, 20-week-old, ad libitumskeletal muscle2323[61]
28miceSirt1-KI, 20-week-old, 60% dietSirt1-KI, 20-week-old, ad libitumskeletal muscle1919[61]
29miceSirt1-KO, 80-week-old, 60% dietSirt1-KO, 80-week-old, ad libitumskeletal muscle721[61]
30miceSirt1-KI, 80-week-old, 60% dietSirt1-KI, 80-week-old, ad libitumskeletal muscle2641[61]
31miceSirt1-KO, 80-week-old, ad libitumwild-type, 80-week-old, ad libitumskeletal muscle1976[61]
32micewild-type, 80-week-old, ad libitumSirt1-KI, 80-week-old, ad libitumskeletal muscle445[61]
33miceSirt1-KO, 20-week-old, ad libitumwild-type, 20-week-old, ad libitumskeletal muscle1152[61]
34micewild-type, 20-week-old, ad libitumSirt1-KI, 20-week-old, ad libitumskeletal muscle135[61]
35miceBPH/2J, hypertensive, aged vesselsBPN/3J, normkidney883[62]
36rabbitunder Goldblatt 2-kidney 1-clipunder sham-operated controlprefrontal cortex229[63]
37chicken1.2% Ca diet: worst health0.8% Ca diet: best healthkidney92[30]
38chicken1% Ca diet, health threshold0.8% Ca diet: best healthkidney83[30]
39chicken1.2% Ca diet: worst health1% Ca diet, health thresholdkidney64[30]
40chicken456-day-old, subfertility224-day-old, fertility peakovary259[34]
41chicken469-day-old, hunger, infertility456-day-old, subfertilityovary926[34]
42chicken469-day-old, hunger, infertility527-day-old, fasting-diet, fertilityovary698[34]
43fruit fly10-day-old, Alzheimer disease-like0-day-old, just post-eclosionhead99[64]
Σ5 species43 human age-related disease models using animals17 tissues37,83422 Refs
Note: NDEG: the number of DEGs; OXIS, Wistar, BPH/2J, and BPH/3J: laboratory animal strains used; MSC(BM): bone-marrow-derived mesenchymal stromal cells; aspirin and fasting: rejuvenators; PolG: DNA polymerase γ, catalytic subunit; Sirt1: sirtuin 1; KO and KI: knock-out and knock-in, respectively.
Table
Table 5. DEGs in the binary “susceptibility versus resistance” models of human ARDs (PubMed data, [75]).
Table 5. DEGs in the binary “susceptibility versus resistance” models of human ARDs (PubMed data, [75]).
#ARD SusceptibilityARD ResistanceTissueNDEGRef.
1renal medullary hypertensionnormrenal medulla13[36]
2pulmonary arterial hypertensionnormblood14[37]
3pulmonary arterial hypertensionnormlung118[38]
4fibrosis in pulmonary hypertensionnormlung3516[39]
5idiopathic pulmonary hypertensionnormlung5639[39]
6nephrosclerosis as kidney agingnormkidney16[40]
7atrial fibrillation as heart agingnormauricle tissue300[41]
8myocardial ischemia as aged heartnormperipheral blood1524[41]
9ALS as aged motoneuronsnormsmall extracellular vesicles402[42]
10FTD as cognitive ageingnormsmall extracellular vesicles164[42]
11ALS as aged motor neuronsnormlarge extracellular vesicles62[42]
12FTD as cognitive ageingnormlarge extracellular vesicles55[42]
13before exercise trainingafter exercise trainingvastus externus170[33]
14LPS-stimulated atherogenesisARID5B-KO as atheroprotectionTHP1 monocytes2542[43]
∑14 binary models of human ARDs10 tissues14,5359 Refs
Note: See the footnote to Table 4. Diseases: ALS, amyotrophic lateral sclerosis; FTD, frontotemporal dementia. LPS, lipopolysaccharide; ARID5B, the AT-rich interaction domain 5B gene.
Table
Table 6. Searching for ARD molecular genetic markers among human genes homologous to 39 novel PAG-related DEGs of the tame and aggressive rats. The number of these homologous DEGs linked to ARD susceptibility and ARD resistance in humans and animals is taken into account.
Table 6. Searching for ARD molecular genetic markers among human genes homologous to 39 novel PAG-related DEGs of the tame and aggressive rats. The number of these homologous DEGs linked to ARD susceptibility and ARD resistance in humans and animals is taken into account.
Rat GeneTotal Number of DEGsBinomial
Distribution		Rat GeneTotal Number of DEGsBinomial Distribution
#SymbolNPC1:
Matching Signs		NPC2:
Opposite Signs		pPADJ#SymbolNPC1:
Matching Signs		NPC2:
Opposite Signs		PPADJ
iiiiiiivvviiiiiiiivVvi
1Amy1a730.171.0021Nmb140.191.00
2Aox1850.291.0022Nmnat1370.171.00
3Ascl3310.311.0023Pcp2510.111.00
4Banp830.111.0024Pdia41060.231.00
5Bdkrb2340.501.0025Pdyn00NDND
6Cartpt110.501.0026Pla2g2c40320.201.00
7Cyp2j10650.501.0027Plod11630.0020.08
8Defb171010.0060.2328Pter230.501.00
9Emx2100.501.0029Pygl490.131.00
10Fat21550.0210.8130Rbm327120.0120.46
11Fcgr2b1710.000070.00531Retsat480.191.00
12Fosb7130.131.0032Rhobtb38180.0381.00
13Gpd1740.271.0033Rln3020.251.00
14Hbb-b110180.091.0034Scel230.501.00
15Hspa1b28210.201.0035Slfn131670.051.00
16Itm2a4170.0040.1436Spint1240.341.00
17Krt227210.241.0037Tnnt1590.211.00
18Morn11220.0060.2538Ucn200.251.00
19Myom21180.321.0039Wsb1330.651.00
20Nfxl1350.361.00
Note: p and PADJ: significance according to the binomial distribution without or with Bonferroni’s correction for multiple comparisons, respectively; ND: not detected; underlined is the only common statistically significant ARD molecular marker found in this work: Fcgr2b. Matching signs: the same direction of expression change; opposite signs: opposite directions of expression change.
Table
Table 7. Statistically significant data on upregulation of Fcγ-receptor-IIb-related DEGs linked to ARD susceptibility and ARD resistance in the subject species.
Table 7. Statistically significant data on upregulation of Fcγ-receptor-IIb-related DEGs linked to ARD susceptibility and ARD resistance in the subject species.
#SpeciesAge-Related Disease
Susceptibility		Age-Related Disease
Resistance		TissueDEGlog2PADJRef.
1rat20-passage-old5-passage-oldMSC(BM)Fcgr2b6.2410−3[35]
2rat20-passage-old5-passage-oldMSC(BM)Fcgr2a3.9910−80[35]
3rat20-passage-old5-passage-oldMSC(BM)Fcgr3a2.8610−44[35]
4rat20-passage-old20-passage-old, AspirinMSC(BM)Fcgr2b1.0010−6[35]
5ratISIAH, hypertensive aged vesselsWAG (norm)adrenal glandFcgr1a0.760.05[51]
6ratISIAH, hypertensive aged vesselsWAG (norm)adrenal glandFcgr3a0.670.05[51]
7mice30-month-old, bone fragility2-month-old, normBoneFcgr2b1.1110−3[55]
8mice30-month-old, bone fragility2-month-old, normBoneFcgr11.1210−2[55]
9mice30-month-old, bone fragility2-month-old, normBoneFcgr30.9210−2[55]
10mice30-month-old, bone fragility2-month-old, normBoneFcgr31.390.05[56]
11micePolG D257A, cardiac disorderwild-type, normheart right ventricleFcgr41.9110−2[58]
12humanfibrosis in pulmonary
hypertension		normLungFCGR1B−1.300.05[39]
13humanidiopathic pulmonary
hypertension		normLungFCGR2A0.270.05[39]
14humanidiopathic pulmonary
hypertension		normLungFCGR3A0.220.05[39]
15humanLPS-stimulated
atherogenesis		ARID5B-KO as
atheroprotection		THP1 monocytesFCGR1A0.4110−4[43]
16humanLPS-stimulated
atherogenesis		ARID5B-KO as
atheroprotection		THP1 monocytesFCGR1B0.5510−4[43]
17humanLPS-stimulated
atherogenesis		ARID5B-KO as
atheroprotection		THP1 monocytesFCGR1C0.3010−2[43]
18humanLPS-stimulated
atherogenesis		ARID5B-KO as
atheroprotection		THP1 monocytesFCGR2A0.4910−5[43]
Note: See footnotes to Table 4 and Table 5.
Table
Table 8. RNA-Seq transcriptomes of domestic animals versus their wild conspecifics (PubMed data [75]).
Table 8. RNA-Seq transcriptomes of domestic animals versus their wild conspecifics (PubMed data [75]).
#Domestic AnimalsWild AnimalsTissueNDEGRef.
1tame ratsaggressive ratshypothalamus46[204]
2tame ratsaggressive ratshippocampus42[74]
3tame ratsaggressive ratsmidbrain tegmentum31[68]
4tame ratsaggressive ratsfrontal cortex20[205]
5guinea pigscavyfrontal cortex883[205]
6domestic rabbitswild rabbitsfrontal cortex17[205]
7domestic rabbitswild rabbitsparietal-temporal cortex216[206]
8domestic rabbitswild rabbitsamygdala118[206]
9domestic rabbitswild rabbitshypothalamus43[206]
10domestic rabbitswild rabbitshippocampus100[206]
11dogswolvesblood450[207]
12dogswolvesfrontal cortex13[205]
13tame foxesaggressive foxespituitary327[208]
14pigsboarsfrontal cortex30[205]
15pigsboarsfrontal cortex34[209]
16pigsboarspituitary22[210]
17domestic chickenwild chickenpituitary474[211]
Σ7 domestic animal species7 wild animal species8 tissues286610 Refs
Table
Table 9. Comparison of the effects of unidirectional changes in the expression (a) of the human FCGR2B gene on ARD severity in humans and (b) of its animal homologs on the microevolutionary events leading to domestic and wild animals.
Table 9. Comparison of the effects of unidirectional changes in the expression (a) of the human FCGR2B gene on ARD severity in humans and (b) of its animal homologs on the microevolutionary events leading to domestic and wild animals.
(a) Humans(b) Animals
Effect of changes in human FCGR2B expression on
ARDs: aggravating (→) or alleviating (←)		Effect of changes in the expression of animal genes homologous to human FCGR2B
deficienteffectexcessiveeffectdeficientexcessivetissueDEGlog2PADJRefs
In South Asia and Africa, the human FCGR2B-deficient alleles have the most frequent occurrence as protectors against infection [117], susceptibility to which increases with age as immunosenescence [118]←FCGR2B has been explored using the “C-type lectin-like molecule-1”/”Fc-domain” fusion protein as a target antigen for chemotherapy against acute myeloid leukemia [119] as a cellular-senescence-related immunogenic disease [120]→aggressive rattame
rat		PAGFcgr2b2.020.05[this work]
aggressive rattame
rat		hypothalamusFcrl21.120.05[204]
aggressive rattame
rat		hypothalamusFcgr3a2.0610−2[204]
aggressive rattame
rat		midbrain
tegmentum		Fcgr2b2.010.05[68]
aggressive foxtame foxpituitaryFcrl10.4310−2[208]
wild
rabbit		domestic rabbitparietal-temporal cortexFcgr3b1.3510−2[206]
guinea pigcavyfrontal cortexFcer2−1.360.05[205]
Note: See footnotes to Table 4 and Table 5. Fcer2: Fc epsilon receptor II; Fcrl1 and Fcrl2: Fc-receptor-like 1 and 2, respectively.
Table
Table 10. Correlations between the effects of unidirectional changes in the expression (a) of the human FCGR2B gene on ARD severity in humans and (b) of its animal homologs on the microevolutionary events leading to domestic and wild animals.
Table 10. Correlations between the effects of unidirectional changes in the expression (a) of the human FCGR2B gene on ARD severity in humans and (b) of its animal homologs on the microevolutionary events leading to domestic and wild animals.
(a) HumansEffect of Changes in Human FCGR2B Expression on ARDsPearson’s χ2 TestFisher’s Exact Test
(b) Animals Alleviating (←)Aggravating (→)χ2p
Effect of changes in the expression of animal homologs to human FCGR2Bwild617.1410−20.05
domestic16
Table
Table 11. qPCR primers selected via the publicly available web service PrimerBLAST [246].
Table 11. qPCR primers selected via the publicly available web service PrimerBLAST [246].
No.GeneForward, 5′→3′Reverse, 5′→3′
Novel DEGs Identified in the PAG of Tame versus Aggressive Adult Male Rats
1Ascl3CCTCTGCTGCCCTTTTCCAGACTTGACTCGCTGCCTCTCT
2Defb17TGGTAGCTTGGACTTGAGGAAAGAATGCAGCAGTGTGTTCCAGGTC
Reference genes
3B2mGTGTCTCAGTTCCACCCACCTTACATGTCTCGGTCCCAGG
4Hprt1TCCCAGCGTCGTGATTAGTGACCTTCATGACATCTCGAGCAAG
5Rpl30CATCTTGGCGTCTGATCTTGTCAGAGTCTGTTTGTACCCC
Note: For the DEGs subjected to this qPCR-based verification, see Table 2; reference rat genes: B2m, β-2-microglobulin [81]; Hprt1, hypoxanthine phosphoribosyltransferase 1 [82]; and Rpl30, ribosomal protein L30 [83].
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Shikhevich, S.; Chadaeva, I.; Khandaev, B.; Kozhemyakina, R.; Zolotareva, K.; Kazachek, A.; Oshchepkov, D.; Bogomolov, A.; Klimova, N.V.; Ivanisenko, V.A.; et al. Differentially Expressed Genes and Molecular Susceptibility to Human Age-Related Diseases. Int. J. Mol. Sci. 2023, 24, 3996. https://doi.org/10.3390/ijms24043996

AMA Style

Shikhevich S, Chadaeva I, Khandaev B, Kozhemyakina R, Zolotareva K, Kazachek A, Oshchepkov D, Bogomolov A, Klimova NV, Ivanisenko VA, et al. Differentially Expressed Genes and Molecular Susceptibility to Human Age-Related Diseases. International Journal of Molecular Sciences. 2023; 24(4):3996. https://doi.org/10.3390/ijms24043996

Chicago/Turabian Style

Shikhevich, Svetlana, Irina Chadaeva, Bato Khandaev, Rimma Kozhemyakina, Karina Zolotareva, Anna Kazachek, Dmitry Oshchepkov, Anton Bogomolov, Natalya V. Klimova, Vladimir A. Ivanisenko, and et al. 2023. "Differentially Expressed Genes and Molecular Susceptibility to Human Age-Related Diseases" International Journal of Molecular Sciences 24, no. 4: 3996. https://doi.org/10.3390/ijms24043996

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop