Редактирование: TRIM59

Tripartite motif-containing protein 59 (RING finger protein 104) (Tumor suppressor TSBF-1) [RNF104] [TRIM57] [TSBF1]

==Publications==

{{medline-entry
|title=DNA methylation of the [[ELOVL2]], [[FHL2]], [[KLF14]], C1orf132/MIR29B2C, and [[TRIM59]] genes for age prediction from blood, saliva, and buccal swab samples.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/30300865
|abstract=Many studies have reported age-associated DNA methylation changes and age-predictive models in various tissues and body fluids. Although age-associated DNA methylation changes can be tissue-specific, a multi-tissue age predictor that is applicable to various tissues and body fluids with considerable prediction accuracy might be valuable. In this study, DNA methylation at 5 CpG sites from the [[ELOVL2]], [[FHL2]], [[KLF14]], C1orf132/MIR29B2C, and [[TRIM59]] genes were investigated in 448 samples from blood, saliva, and buccal swabs. A multiplex methylation SNaPshot assay was developed to measure DNA methylation simultaneously at the 5 CpG sites. Among the 5 CpG sites, 3 CpG sites in the [[ELOVL2]], [[KLF14]] and [[TRIM59]] genes demonstrated strong correlation between DNA methylation and age in all 3 sample types. Age prediction models built separately for each sample type using the DNA methylation values at the 5 CpG sites showed high prediction accuracy with a Mean Absolute Deviation from the chronological age (MAD) of 3.478 years in blood, 3.552 years in saliva and 4.293 years in buccal swab samples. A tissue-combined model constructed with 300 training samples including 100 samples from each blood, saliva and buccal swab samples demonstrated a very strong correlation between predicted and chronological ages (r = 0.937) and a high prediction accuracy with a MAD of 3.844 years in the 148 independent test set samples of 50 blood, 50 saliva and 48 buccal swab samples. Although more validation might be needed, the tissue-combined model's prediction accuracies in each sample type were very much similar to those obtained from each tissue-specific model. The multiplex methylation SNaPshot assay and the age prediction models in our study would be useful in forensic analysis, which frequently involves DNA from blood, saliva, and buccal swab samples.
|mesh-terms=* Acetyltransferases
* Adolescent
* Adult
* Aged
* Aging
* Blood Chemical Analysis
* CpG Islands
* DNA Methylation
* Fatty Acid Elongases
* Forensic Genetics
* Genetic Markers
* Genotyping Techniques
* Humans
* Intracellular Signaling Peptides and Proteins
* Kruppel-Like Transcription Factors
* LIM-Homeodomain Proteins
* Membrane Proteins
* Metalloproteins
* Middle Aged
* Mouth Mucosa
* Muscle Proteins
* Saliva
* Sequence Analysis, DNA
* Sp Transcription Factors
* Transcription Factors
* Tripartite Motif Proteins
* Young Adult
|keywords=* Age
* Blood
* Buccal swab
* DNA methylation
* Methylation SNaPshot
* Saliva
|full-text-url=https://sci-hub.do/10.1016/j.fsigen.2018.09.010
}}
{{medline-entry
|title=Proof of concept study of age-dependent DNA methylation markers across different tissues by massive parallel sequencing.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/30031222
|abstract=The use of DNA methylation (DNAm) for chronological age determination has been widely investigated within the last few years for its application within the field of forensic genetics. The majority of forensic studies are based on blood, saliva, and buccal cell samples, respectively. Although these types of samples represent an extensive amount of traces found at a crime scene or are readily available from individuals, samples from other tissues can be relevant for forensic investigations. Age determination could be important for cases involving unidentifiable bodies and based on remaining soft tissue e.g. brain and muscle, or completely depend on hard tissue such as bone. However, due to the cell type specificity of DNAm, it is not evident whether cell type specific age-dependent CpG positions are also applicable for age determination in other cell types. Within this pilot study, we investigated whether 13 previously selected age-dependent loci based on whole blood analysis including amongst others [[ELOVL2]], [[TRIM59]], [[F5]], and [[KLF14]] also have predictive value in other forensically relevant tissues. Samples of brain, bone, muscle, buccal swabs, and whole blood of 29 deceased individuals (age range 0-87 years) were analyzed for these 13 age-dependent markers using massive parallel sequencing. Seven of these loci did show age-dependency in all five tissues. The change of DNAm during lifetime was different in the set of tissues analyzed, and sometimes other CpG sites within the loci showed a higher age-dependency. This pilot study shows the potential of existing blood DNAm markers for age-determination to analyze other tissues than blood. We identified seven known blood-based DNAm markers for use in muscle, brain, bone, buccal swabs, and blood. Nevertheless, a different reference set for each tissue is needed to adapt for tissue-specific changes of the DNAm over time.
|mesh-terms=* Adolescent
* Adult
* Aged
* Aged, 80 and over
* Aging
* Bone and Bones
* Brain Chemistry
* Child
* Child, Preschool
* CpG Islands
* DNA Methylation
* Female
* Genetic Markers
* High-Throughput Nucleotide Sequencing
* Humans
* Infant
* Infant, Newborn
* Linear Models
* Male
* Middle Aged
* Mouth Mucosa
* Muscle, Skeletal
* Pilot Projects
* Polymerase Chain Reaction
* Polymorphism, Single Nucleotide
* Proof of Concept Study
* Saliva
* Sequence Analysis, DNA
* Young Adult
|keywords=* Age determination
* DNA methylation
* Forensic epigenetics
* Massive parallel sequencing
|full-text-url=https://sci-hub.do/10.1016/j.fsigen.2018.07.007
}}
{{medline-entry
|title=Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/28841467
|abstract=The use of DNA methylation (DNAm) to obtain additional information in forensic investigations showed to be a promising and increasing field of interest. Prediction of the chronological age based on age-dependent changes in the DNAm of specific CpG sites within the genome is one such potential application. Here we present an age-prediction tool for whole blood based on massive parallel sequencing (MPS) and a random forest machine learning algorithm. MPS allows accurate DNAm determination of pre-selected markers and neighboring CpG-sites to identify the best age-predictive markers for the age-prediction tool. 15 age-dependent markers of different loci were initially chosen based on publicly available 450K microarray data, and 13 finally selected for the age tool based on MPS (DDO, [[ELOVL2]], [[F5]], [[GRM2]], [[HOXC4]], [[KLF14]], [[LDB2]], [[MEIS1]]-AS3, [[NKIRAS2]], [[RPA2]], [[SAMD10]], [[TRIM59]], ZYG11A). Whole blood samples of 208 individuals were used for training of the algorithm and a further 104 individuals were used for model evaluation (age 18-69). In the case of [[KLF14]], [[LDB2]], [[SAMD10]], and [[GRM2]], neighboring CpG sites and not the initial 450K sites were chosen for the final model. Cross-validation of the training set leads to a mean absolute deviation (MAD) of 3.21 years and a root-mean square error (RMSE) of 3.97 years. Evaluation of model performance using the test set showed a comparable result (MAD 3.16 years, RMSE 3.93 years). A reduced model based on only the top 4 markers ([[ELOVL2]], [[F5]], [[KLF14]], and [[TRIM59]]) resulted in a RMSE of 4.19 years and MAD of 3.24 years for the test set (cross validation training set: RMSE 4.63 years, MAD 3.64 years). The amplified region was additionally investigated for occurrence of SNPs in case of an aberrant DNAm result, which in some cases can be an indication for a deviation in DNAm. Our approach uncovered well-known DNAm age-dependent markers, as well as additional new age-dependent sites for improvement of the model, and allowed the creation of a reliable and accurate epigenetic tool for age-prediction without restriction to a linear change in DNAm with age.
|mesh-terms=* Adolescent
* Adult
* Aged
* Aging
* Algorithms
* CpG Islands
* DNA Methylation
* Genetic Markers
* High-Throughput Nucleotide Sequencing
* Humans
* Machine Learning
* Middle Aged
* Polymerase Chain Reaction
* Polymorphism, Single Nucleotide
* Sequence Analysis, DNA
* Young Adult
|keywords=* Age prediction
* DNA methylation
* Machine learning
* Massive parallel sequencing
|full-text-url=https://sci-hub.do/10.1016/j.fsigen.2017.07.015
}}
{{medline-entry
|title=DNA methylation in [[ELOVL2]] and C1orf132 correctly predicted chronological age of individuals from three disease groups.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/28725932
|abstract=Improving accuracy of the available predictive DNA methods is important for their wider use in routine forensic work. Information on age in the process of identification of an unknown individual may provide important hints that can speed up the process of investigation. DNA methylation markers have been demonstrated to provide accurate age estimation in forensics, but there is growing evidence that DNA methylation can be modified by various factors including diseases. We analyzed DNA methylation profile in five markers from five different genes ([[ELOVL2]], C1orf132, [[KLF14]], [[FHL2]], and [[TRIM59]]) used for forensic age prediction in three groups of individuals with diagnosed medical conditions. The obtained results showed that the selected age-related CpG sites have unchanged age prediction capacity in the group of late onset Alzheimer's disease patients. Aberrant hypermethylation and decreased prediction accuracy were found for [[TRIM59]] and [[KLF14]] markers in the group of early onset Alzheimer's disease suggesting accelerated aging of patients. In the Graves' disease patients, altered DNA methylation profile and modified age prediction accuracy were noted for [[TRIM59]] and [[FHL2]] with aberrant hypermethylation observed for the former and aberrant hypomethylation for the latter. Our work emphasizes high utility of the [[ELOVL2]] and C1orf132 markers for prediction of chronological age in forensics by showing unchanged prediction accuracy in individuals affected by three diseases. The study also demonstrates that artificial neural networks could be a convenient alternative for the forensic predictive DNA analyses.
|mesh-terms=* Acetyltransferases
* Adolescent
* Adult
* Aged
* Aging
* Alzheimer Disease
* Case-Control Studies
* Child
* Child, Preschool
* CpG Islands
* DNA Methylation
* Fatty Acid Elongases
* Female
* Forensic Genetics
* Genetic Markers
* Graves Disease
* Humans
* Intracellular Signaling Peptides and Proteins
* Kruppel-Like Transcription Factors
* LIM-Homeodomain Proteins
* Male
* Membrane Proteins
* Metalloproteins
* Middle Aged
* Multivariate Analysis
* Muscle Proteins
* Neural Networks, Computer
* Sp Transcription Factors
* Transcription Factors
* Tripartite Motif Proteins
* Young Adult
|keywords=* Alzheimer’s disease
* Chronological age
* DNA methylation
* Graves’ disease
* Neural networks
* Prediction accuracy
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5748441
}}
{{medline-entry
|title=Independent validation of DNA-based approaches for age prediction in blood.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/28511095
|abstract=Numerous molecular biomarkers have been proposed as predictors of chronological age. Among them, T-cell specific DNA rearrangement and DNA methylation markers have been introduced as forensic age predictors in blood because of their high prediction accuracy. These markers appear highly promising, but for better application to forensic casework sample analysis the proposed markers and genotyping methods must be tested further. In the current study, signal-joint T-cell receptor excision circles (sjTRECs) and DNA methylation markers located in the [[ELOVL2]], C1orf132, [[TRIM59]], [[KLF14]], and [[FHL2]] genes were reanalyzed in 100 Korean blood samples to test their associations with chronological age, using the same analysis platform used in previous reports. Our study replicated the age association test for sjTREC and DNA methylation markers in the 5 genes in an independent validation set of 100 Koreans, and proved that the age predictive performance of the previous models is relatively consistent across different population groups. However, the extent of age association at certain CpG loci was not identical in the Korean and Polish populations; therefore, several age predictive models were retrained with the data obtained here. All of the 3 models retrained with DNA methylation and/or sjTREC data have a CpG site each from the [[ELOVL2]] and [[FHL2]] genes in common, and produced better prediction accuracy than previously reported models. This is attributable to the fact that the retrained model better fits the existing data and that the calculated prediction accuracy could be higher when the training data and the test data are the same. However, it is notable that the combination of different types of markers, i.e., sjTREC and DNA methylation, improved prediction accuracy in the eldest group. Our study demonstrates the usefulness of the proposed markers and the genotyping method in an independent dataset, and suggests the possibility of combining different types of DNA markers to improve prediction accuracy.
|mesh-terms=* Acetyltransferases
* Aging
* Asian Continental Ancestry Group
* CpG Islands
* DNA Methylation
* Fatty Acid Elongases
* Genetic Markers
* Genotyping Techniques
* Humans
* Intracellular Signaling Peptides and Proteins
* Kruppel-Like Transcription Factors
* LIM-Homeodomain Proteins
* Membrane Proteins
* Metalloproteins
* Muscle Proteins
* Receptors, Antigen, T-Cell
* Republic of Korea
* Sp Transcription Factors
* Transcription Factors
* Tripartite Motif Proteins
|keywords=* Age prediction
* Blood
* DNA methylation
* Forensic science
* Korean
* sjTREC
|full-text-url=https://sci-hub.do/10.1016/j.fsigen.2017.04.020
}}
{{medline-entry
|title=Donor age and C1orf132/MIR29B2C determine age-related methylation signature of blood after allogeneic hematopoietic stem cell transplantation.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/27602173
|abstract=Our recent study demonstrated that DNA methylation status in a set of CpGs located in [[ELOVL2]], C1orf132, [[TRIM59]], [[KLF14]], and [[FHL2]] can accurately predict calendar age in blood. In the present work, we used these markers to evaluate the effect of allogeneic hematopoietic stem cell transplantation (HSCT) on the age-related methylation signature of human blood. DNA methylation in 32 CpGs was investigated in 16 donor-recipient pairs using pyrosequencing. DNA was isolated from the whole blood collected from recipients 27-360 days (mean 126) after HSCT and from the donors shortly before the HSCT. It was found that in the recipients, the predicted age did not correlate with their calendar age but was correlated with the calendar age (r = 0.94, p = 4 × 10(-8)) and predicted age (r = 0.97, p = 5 × 10(-10)) of a respective donor. Despite this strong correlation, the predicted age of a recipient was consistently lower than the predicted age of a donor by 3.7 years (p = 7.8 × 10(-4)). This shift was caused by hypermethylation of the C1orf132 CpGs, for C1orf132 CpG_1. Intriguingly, the recipient-donor methylation difference correlated with calendar age of the donor (r = 0.76, p = 6 × 10(-4)). This finding could not trivially be explained by shifts of the major cellular factions of blood. We confirm the single previous report that after HSCT, the age of the donor is the major determinant of age-specific methylation signature in recipient's blood. A novel finding is the unique methylation dynamics of C1orf132 which encodes MIR29B2C implicated in the self-renewing of hematopoietic stem cells. This observation suggests that C1orf132 could influence graft function after HSCT.
|mesh-terms=* DNA Methylation
* Hematopoietic Stem Cell Transplantation
* Humans
* Tissue Donors
|keywords=* Aging
* Allogeneic hematopoietic stem cell transplantation
* DNA methylation
* MIR29B2C
* Rejuvenation
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012039
}}
{{medline-entry
|title=Development of a forensically useful age prediction method based on DNA methylation analysis.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/26026729
|abstract=Forensic DNA phenotyping needs to be supplemented with age prediction to become a relevant source of information on human appearance. Recent progress in analysis of the human methylome has enabled selection of multiple candidate loci showing linear correlation with chronological age. Practical application in forensic science depends on successful validation of these potential age predictors. In this study, eight DNA methylation candidate loci were analysed using convenient and reliable pyrosequencing technology. A total number of 41 CpG sites was investigated in 420 samples collected from men and women aged from 2 to 75 years. The study confirmed correlation of all the investigated markers with human age. The five most significantly correlated CpG sites in [[ELOVL2]] on 6p24.2, C1orf132 on 1q32.2, [[TRIM59]] on 3q25.33, [[KLF14]] on 7q32.3 and [[FHL2]] on 2q12.2 were chosen to build a prediction model. This restriction allowed the technical analysis to be simplified without lowering the prediction accuracy significantly. Model parameters for a discovery set of 300 samples were R(2)=0.94 and the standard error of the estimate=4.5 years. An independent set of 120 samples was used to test the model performance. Mean absolute deviation for this testing set was 3.9 years. The number of correct predictions ±5 years achieved a very high level of 86.7% in the age category 2-19 and gradually decreased to 50% in the age category 60-75. The prediction model was deterministic for individuals belonging to these two extreme age categories. The developed method was implemented in a freely available online age prediction calculator. 
|mesh-terms=* Adolescent
* Adult
* Aged
* Aging
* Child
* Child, Preschool
* CpG Islands
* DNA
* DNA Methylation
* Female
* Forensic Genetics
* Humans
* Male
* Middle Aged
* Predictive Value of Tests
|keywords=* DNA methylation
* DNA-based age prediction
* Forensic science
* Prediction modelling
|full-text-url=https://sci-hub.do/10.1016/j.fsigen.2015.05.001
}}