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Paraoxonases are a family of three enzymes called PON1, PON2 and PON3. They have multifunctional roles in various biochemical pathways such as protection against oxidative damage and lipid peroxidation, contribution to innate immunity, detoxification of reactive molecules, bioactivation of drugs, modulation of endoplasmic reticulum stress and regulation of cell proliferation/apoptosis. Since they are able to perform multiple autonomous and often unrelated functions, they are considered as “moonlighting proteins” [1]. PON1 is the most studied enzyme of the family. It is synthesized primarily in the liver and appears mainly in serum, where is associated to high-density lipoproteins (HDL) [2]. PON2 is located intracellularly and PON3, although appears also in serum, is around 2 orders of magnitude less abundant than PON1 [3].
PON1 has been widely studied in human medicine with excellent reviews produced by different research groups [4, 5, 6, 7]. Initially the interest on this enzyme arose from the toxicological point of view, by its protective role from poisoning by organophosphate derivates. But more recently research has been focused on other clinical aspects such as protective role in vascular disease as well as its use as biomarker of diseases involving mainly three situations: (a) oxidative stress, since PON1 protects against oxidation [8]; (b) inflammation, being considered PON1 as a negative acute phase protein [9] and (c) liver diseases, because PON1 is synthesized in this organ [10]. In veterinary medicine, studies of PON1 have been traditionally focused in bovine [11, 12], however in recent years it has gained interest in other species such as dogs [13, 14], cats [15] and horses [16].
PON1 can be measured based on its activity by spectrophotometric assays and also can be directly quantified by immunological methods using specific antibodies [17]. The spectrophotometric assays based on the ability of PON1 to hydrolyse substrates are currently more widely used, probably due to their low cost and availability. PON1 was named paraoxonase for its ability to hydrolyze paraoxon (diethyl p-nitrophenyl phosphate, E600), the toxic oxon metabolite of parathion [18]. However PON1 is considered as a promiscuous enzyme having also the ability to hydrolyze many other substrates such as other organophosphorous compounds, non phosphorous arylesters and also lactones, which have been considered as its primary substrates [19].
PON1 can be evaluated by its different activities, for example its paraxonase activity (when paraoxon is used as substrate), arylesterase activity (when a non phosphorous arylester such as phenyl acetate or 4 (p)-nitrophenyl acetate is used as substrate) or by its lactonase activity (when 5-thiobutil butyrolactone –TBBL- or other lactones such as dihydrocoumarin are used as substrate). However, this terminology seems not to be totally accurate from the chemistry point of view, because “aryl” refers to any functional group or substituent derived from an aromatic ring; and therefore paraoxon could be also considered as an arylester and included in the group of the arylesterases. Maybe it would be more appropriate to use the general term “PON1 activity” that can be measured by different substrates such as paroxon, phenyl acetate, 4-nitrophenyl acetate, TBBL or dihydrocoumarin. These substrates (Table 1) are currently the most frequently used. However there are other substrates such as chlorpyrifos, diazinon, sarin or soman in the group of organophosphorous compounds [20] and other different lactones [19, 21] that can be also employed in PON1 assays.Table 1Chemical structure of the most frequently used substrates for PON1 assays
Paraoxon | Phenyl acetate | 4-nitrophenyl acetate | TBBL | Dihydrocoumarin |
|---|
Currently in PON1 measurements described in literature, there is a great heterogeneity in the selection of the substrates as well in the conditions of the assays used with each substrate. This situation can lead to difficulties in the comparison of values obtained by the different laboratories, generation of inappropriate results for not using adequate analytical procedures, or problems in interpretation due to the different substrate activity polymorphisms. This report will deal with two main topics: (a) characteristics of the different substrates that can be used for measuring PON1 activity with a special focus on the most widely used and (b) the technical aspects of PON1 assays.
Abstract
Reliable methodology for predicting the age of mature dogs is currently unavailable. Inthis study, amplicon sequencing of 50 blood samples obtained from diseased dogs was usedto measure methylation in seven DNA regions. Significant correlations between methylationlevel and age were identified in four of the seven regions. These four regions were thentested in samples from 31 healthy toy poodles, and correlations were detected in tworegions. The age of another 11 dogs was predicted using data from the diseased dogs andthe healthy poodles. The mean difference between the actual and calculated ages was 34.3and 23.1 months, respectively. Further research is needed to identify additional sites ofage-related methylation and allow accurate age prediction in dogs.
The ability to accurately predict the age of an adult dog would provide useful informationfor those involved in veterinary medicine and animal science. For example, companion animalveterinarians take age into consideration when making diagnoses and determining treatmentplans. In animal shelters, most people hope to know the age of the animal before they adopt.However, the age of a dog is not always self-evident in situations where breeding isuncontrolled. Several molecular biology-based age-prediction methods have been reported inhumans; these include quantitative analysis of T-cell receptor excision circles [, , , ],mitochondrial DNA fragment deletion [, , , , ], telomereDNA fragment shortening [, ] and age-related DNA methylation levels at specific genomic loci [, , , , ]. Although radiography can be used to predict the age ofyoung dogs (less than 1 year old) by their skeletal development [, ], the age of adult dogs isusually predicted by subjective observations of characteristics, such as overall appearance ortooth abrasion. Previously, we attempted molecular biology-based age-prediction by measuringthe level of blood signal joint T-cell receptor excision circles (sjTREC) in dogs []; sjTREC levels reflect the number of T-cells freshlyrecruited from the thymus and thus potentially indicate age-related thymic atrophy. However,we found no significant correlation between sjTREC levels and age, probably owing to theprocess of thymic involution that occurs at an early stage of life in dogs. Althoughage-related telomere length shortening in normal mammary gland tissue has been reported [], age prediction from the measurement of telomere lengthhas not been attempted in dogs.
DNA methylation is characterized by the addition of a methyl group to a cytosine nucleotideprimarily at cytosine-phosphate-guanine (CpG) sites. Short DNA elements that have a muchhigher density of CpG sites, so-called CpG islands, are often located near transcription startsites. Hypermethylation of these regions is usually associated with transcriptional silencing.With increasing age, some gene-specific CpG dinucleotides can become hypermethylated orhypomethylated []. These age-related methylationchanges have been used as a biological marker for forensic age-prediction in humans and mayhave the potential to be used for predicting age in dogs. Recently, several studies have triedto use methylation-predicted age in humans as an indicator of risk of age-related diseases andmortality [, ]. In humans, several CpG sites have been identified as age-related markers [, , ], and quantitative analysis of the methylation levels ofmultiple genomic regions simultaneously makes age-prediction practical with an averageaccuracy of 4 to 10 years [, , , , ]. The aim of the presentstudy was to identify age-related methylation sites in dogs. We selected loci for testingbased on the findings of human studies [, ].
In the first experiment, blood samples were obtained from 50 dogs; forty-five of these wereclient-owned, diseased dogs that were brought to the Kagoshima University Veterinary TeachingHospital for veterinary care, and five were healthy dogs owned by faculty staff. Genomic DNAwas extracted from 100 to 200 µl of EDTA-K2-treated blood using a DNeasyBlood & Tissue Kit (QIAGEN, Venlo, The Netherlands). The eluted DNA solution wasconcentrated using a DNA Clean & Concentrator-5 Kit (Zymo Research, Irvine, CA, U.S.A.) ifnecessary. Bisulfite treatment of DNA was performed using an EZ DNA Methylation-Gold Kit (ZymoResearch) according to the manufacturer’s instructions, and treated DNA was used as a templatefor PCR. The genomic region of interest was selected to analyze the methylation levels of CpGregions that correlated to the regions showing age-related methylation changes in humans[, ]. Briefly,approximately 1,000 bases flanking previously reported age-related methylation sites wereretrieved from the human genome assembly hg38 using the UCSC genome browser(https://genome.ucsc.edu/cgi-bin/hgGateway). Homologous regions in the canine genome wereidentified using the Basic Local Alignment Search Tool (BLAST) from the NCBI website(https://blast.ncbi.nlm.nih.gov/Blast.cgi). Homologous DNA regions in the canine genome wereidentified for some, but not all, of the human age-related regions. In total, 11 genomicregions were identified for analysis in the canine genome. Oligonucleotide PCR primers weredesigned for bisulfite-treated DNA (converted DNA) using the Methprimer web tool(http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi) and were synthesized commercially(FASMAC, Atsugi, Japan). The nucleotide sequences of these primers, and their locations withinthe canine reference genome, are shown in Table1. Adapter nucleotides were added to the 5ʹ end of these PCR primers to provideprimer binding sites for second-round PCR. First-round PCR was performed using the Epitaq HSPCR Kit (Takara, Kusatsu, Japan) according to the manufacturer’s instructions. Second-roundPCR was performed using the GoTaq Hot Start Colorless Master Mix (Promega, Madison, WI,U.S.A.) to add a nucleotide adapter for next-generation sequence analysis and nucleotide tagsfor individual discrimination from mixed samples. PCR products were electrophoresed andextracted from agarose gels, and then purified using the High Pure PCR Product PurificationKit (Roche Diagnostics, Mannheim, Germany).
Table 1.
| Primer name | Gene specific primer nucleotide sequence (5′-3′) | Dog chromosome (Chr), NCBI reference ID, Targetregion, PCR product size (bp)a) | Number of CpG sites in the target region | Adjacent gene and remarks | Corresponding age-related CpG site in human (ref.) |
|---|---|---|---|---|---|
| A-sense | ttttgggagttttggtgagaa | Chr 5, NC_006587.3, 67230026–67230305, 280 | 15 | GSE1 | cg07082267 [] |
| A-reverse | ataaaaaaaaccccataaatct | ||||
| C-sense | gtttttttatgaatgaatattga | Chr 6, NC_006588.3, 8701204–8701368,165 | 17 | VGF | cg21186299 [] |
| C-reverse | aataaattaaactcaactaaatc | ||||
| E-sense | ggtaaggagaggaggtagttttagg | Chr 24, NC_006606.3, 33293646–33294073, 428 | 31 | SLC12A5 | cg07547549 [] |
| E-reverse | ccccacctttcaactaaaaatct | ||||
| F-sense | ggtgtttaaagtaaattagagagt | Chr 35, NC_006617.3, 23602291–23602560, 270 | 21 | SCGN | cg06493994 [, ] |
| F-reverse | tccaaatcctttcaaaaaaacta | ||||
| H-sense | gttaaattttgtttaatttgttgtg | Chr 10, NC_006592.3, 49672242–49672424, 183 | 13 | KCNK12 | cg27320127 [] |
| H-reverse | aaatcctttcccccaaaaaaacc | ||||
| I-sense | ggtttttattattaaggatttttttt | Chr 3, NC_006585.3, 37548040–37548332, 293 | 13 | OTUD7A | cg01763090 [] |
| I-reverse | aaactacaaatttctttatttctcttatta | (3′UTR) | |||
| J-sense | ggagtttaataggggagagagattt | Chr 5, NC_006587.3, 32051030–32051450, 421 | 36 | BCL6B | cg10137837 [] |
| J-reverse | taaaacccctccaaaatacctaac | ||||
| K-sense | ttatatagtggggagaaaggtaagtt | Chr 15, NC_006597.3, 45028967–45029372, 406 | 32 | POU4F2 | cg05991454 [] |
| K-reverse | accctaaaactaaacactaaaatcc | ||||
| Adapter nucleotide sequence (5′-3′) | Remarks | ||||
| Senseb) | acactctttccctacacgacgctcttccgatct-(Primer) | Adapter for sense primer | |||
| Reverse | gtgactggagttcagacgtgtgctcttccgatct-(Primer) | Adapter for reverse primer | |||
a) PCR product size was predicted from the gene-specific DNA fragment (excludingadapter nucleotides). b) Adapter nucleotides were added to the 5′ end of eachgene-specific primer.
We failed to amplify DNA fragments from four of the 11 regions. The PCR products from theseven successfully amplified genomic regions were submitted either for 250- or 300-bp pair-endMiseq analysis (Illumina, San Diego, CA, U.S.A.) at FASMAC Co., Ltd., depending on the lengthof the amplicon. Sequence data from Miseq analysis were processed and then aligned usingU-gene software (Unipro, Novosibirsk, Russia) to find specific, highly methylated CpG sites.The numbers of changed (unmethylated) and unchanged (methylated) CpGs at each site werecounted individually using a textedit word processor (Apple, Cupertino, CA, U.S.A.). Onlyregions with more than 200 sequences per individual were included for further analysis. Themethylation rate was calculated as the number of unchanged CpGs divided by the sum of changedand unchanged CpGs for each site. Pearson’s correlation coefficients were calculated to assessthe correlation between each CpG site and the age of the dog. The CpG site with the highestcorrelation coefficient was selected for each DNA region (Table 2). A scatter plot of the methylation rate at each CpG site against age, with astraight-line approximation, is shown in Fig.1. There was a significant correlation between methylation levels and age at four of theseven CpG sites (P<0.05, Pearson’s correlation coefficient). NeighboringCpG sites in the same DNA region with the four sites showed 90% (37/41 sites) agreement intheir slope direction with age as reported in human cases [].
Table 2.
| Region name | NCBI reference ID | CpG position in reference | Adjacent gene | Correlation coefficient (r)a) | P-value |
|---|---|---|---|---|---|
| A | NC_006587.3 | 67,230,027 | GSE1 | –0.367 | 0.009 |
| C | NC_006588.3 | 8,701,563 | VGF | –0.195 | 0.175 |
| F | NC_006617.3 | 23,602,365 | SCGN | 0.291 | 0.040 |
| H | NC_006592.3 | 49,672,518 | KCNK12 | 0.150 | 0.297 |
| I | NC_006585.3 | 37,548,075 | OTUD7A | 0.194 | 0.196 |
| J | NC_006587.3 | 32,051,190 | BCL6B | –0.354 | 0.013 |
| K | NC_006597.3 | 45,029,058 | POU4F2 | 0.421 | 0.003 |
a) Pearson’s correlation coefficient.
Scatter plots of age (months) and methylation rates of CpG sites with the highestcorrelation coefficients in seven DNA regions (A, C, F, H, I, J and K) in the caninegenome. These seven regions were expected to show age-related methylation changes basedon human studies [, ]. The name of the adjacent gene in the canine genome is given as theheader label for each scatter plot. Straight-line approximations have been applied toeach scatter plot, and Pearson’s correlation coefficients are shown.
We then examined the methylation levels at the four CpG sites with significant correlationsbetween age and methylation in clinically healthy dogs. Blood samples were obtained from 31toy poodles at the Nishi Animal Hospital (Kagoshima, Japan) or at the Harada-gakuen AnimalSchool (Kagoshima, Japan). Methylation levels were measured as described for the previoussamples. There were significant correlations between methylation level and age at two of thefour CpG sites that were analyzed (Fig. 2). We then attempted age prediction using another 11 client-owned dogs brought toKagoshima University for veterinary care. Age was predicted by multiple regression analysis ofthe four CpG sites from the initial 50 client-owned dogs or the 2 CpG sites from the 31healthy toy poodles (Table 3). The mean absolute difference between actual and predicted age, calculatedusing multiple regression of data from four CpG sites, was 34.3 months. In five of the 11dogs, the difference between actual and predicted age was less than 24 months. The meanabsolute difference between actual and predicted age calculated using data from two CpG siteswas 23.1 months. The difference between actual and predicted age was less than 24 months inseven of the 11 dogs. The maximum difference between actual and predicted age was observed ina 140 month-old dog whose predicted age based on methylation of two CpG sites was 70.8 months.Marked diremption was observed in some dogs. Factors (e.g. nutritional state, breed anddisease) that influence methylation levels should be identified. Further exhaustive research,such as genome-wide methylation sequence analysis or development of a cost effectivemethylation array assay, may identify other, more accurate, age-related changes in themethylation of the canine genome. Consolidation of public databases of canine genomemethylation may also help to identify other age-related methylation changes, as has been shownin humans [].
Scatter plots of age (months) and methylation rates of CpG sites in four DNA regions(A, F, J and K) in blood samples obtained from 31 clinically healthy toy poodles. Thename of the adjacent gene in the canine genome is given as the header label.Straight-line approximations were applied to each scatter plot, and Pearson’scorrelation coefficients (r) and P values are shown.
Table 3.
| Case No. | Actual age (months) | Predicted age from 4 CpG sites (months)a) | Difference (months) | Predicted age from 2 CpG sites (months)b) | Difference (months) |
|---|---|---|---|---|---|
| 1 | 140 | 50.3 | –89.7 | 70.8 | –69.2 |
| 2 | 50 | 55.4 | 5.4 | 86.8 | 36.8 |
| 3 | 130 | 26.1 | –103.9 | 118.8 | –11.2 |
| 4 | 15 | 18.4 | 3.4 | 45.3 | 30.3 |
| 5 | 115 | 73.7 | –41.3 | 129.7 | 14.7 |
| 6 | 124 | 106.4 | –17.6 | 121.4 | –2.6 |
| 7 | 120 | 77.6 | –42.4 | 96.0 | –24.0 |
| 8 | 78 | 86.2 | 8.2 | 97.0 | 19.0 |
| 9 | 110 | 82.0 | –28.0 | 90.3 | –19.7 |
| 10 | 125 | 93.7 | –31.3 | 106.2 | –18.8 |
| 11 | 75 | 69.0 | –6.0 | 82.3 | 7.3 |
| Mean absolute Difference | 34.3 | 23.1 | |||
| Standard deviation (SD) | 37.2 | 30.0 | |||
a) Age was predicted by multiple regression analysis using methylation data at 4 CpGsites from 50 dogs (mostly diseased dogs). b) Age was predicted by multiple regressionanalysis using methylation data at 2 CpG sites from 31 clinically healthy toy.
The biological relevance of some of the genes adjacent to the CpG sites that we studied hasbeen reported, while the function of others, such as Genetic suppressor element 1 (GSE1), islargely unknown. Secretagogin (SCGN) is a calcium binding protein. A negative correlationbetween SCGN mRNA expression in peripheral blood mononuclear cells and age has been reported[]; this age-related decrease in mRNA expression ispossibly regulated by epigenetic changes. B-cell CLL/lymphoma 6 member B protein (BCL6B) is atranscription repressor and a potential tumor suppressor. Epigenetic silencing of BCL6B hasbeen reported in human hepatocellular carcinoma [].Although expression of BCL6B by CD8 positive T cells has been reported [], its association with aging is unknown. Interestingly, the correlationbetween age and methylation observed in dogs at the BCL6B CpG site was the inverse of thatfound in humans []. The reason for this is unclear, andage-related BCL6B expression in dogs needs a further study. POU domain, class 4, transcriptionfactor 2 (POU4F2) is a transcription factor, and hypermethylation of POU4F2 CpG sites has beenreported in some tumors [, ], but its biological relevance in aging has not been reported.
Age-prediction by DNA methylation levels has been reported in humans using various tissuesamples, including saliva, teeth and brain [, , , ]. However, blood sample are used in most studies,because they are easily obtained. The leukocyte subtype composition of the blood sample doesnot seem to affect the accuracy of the age prediction []. Thus, in this study, we examined methylation levels in dogs using peripheralblood samples. A major limitation of this study is the potential bias in the samples used inthe first experiment, which were obtained mostly from sick dogs. Poor health status,especially in the case of age-related diseases, may cause methylation patterns that mimicage-related changes in methylation. A recent study in humans found that some age-relatedmethylation changes become insignificant after restricting the study sample to those withouthistory of major age-related diseases, such as diabetes mellitus, cardiovascular disease,stroke and cancer []. Our first experiment includeddogs with age-related diseases, such as diabetes mellitus (n=4), cardiovascular disease (n=5)and tumors (n=12). Although the four age-related CpG sites identified in the first analysiswere homologous to regions showing age-related changes in humans without age-related diseases[], the correlations between methylation and age intwo of the four sites were not significant in our second experiment using only healthy toypoodles. Ideal age markers should be minimally affected by environmental or geneticfactors.
In conclusion, we measured the methylation levels at selected CpG sites in dogs usingnext-generation amplicon sequencing and identified some age-related changes in methylation.Although age predictions made using the methylation levels at these CpG sites are not yetsufficiently accurate for practical use, further research to identify other age-relatedmethylation sites may make accurate age prediction possible.
Acknowledgments
The authors are grateful to Drs. Hiroshi Nishi (Nishi Animal Hospital,Kagoshima, Japan) and Takako Nagayoshi (Harada-gakuen, Kagoshima, Japan) for providing theblood samples.