Epigenetic clock in the aorta and age-related endothelial dysfunction in mice.

While epigenetic age (EA) of mouse blood can be determined using DNA methylation analysis at three CpG sites in the Prima1, Hsf4 and Kcns1 genes it is not known whether this approach is useful for predicting vascular biological age. In this study we validated the 3-CpG estimator for age prediction in mouse blood, developed a new predictive model for EA in mouse aorta, and assessed whether epigenetic age acceleration (EAA) measured with blood and aorta samples correlates with age-dependent endothelial dysfunction. Endothelial function was characterized in vivo by MRI in 8-96-week-old C57BL/6 mice. Arterial stiffness was measured by USG-doppler. EA-related changes within 41 CpG sites in Prima1, Kcns1 and Hsf4 loci, were analyzed in the aorta and blood using bisulfite amplicon high-throughput sequencing. Progressive age-dependent endothelial dysfunction and changes in arterial stiffness were observed in 36-96-week-old C57BL/6 mice. Methylation levels of the investigated loci correlated with chronological age in blood and the aorta. The new model for EA estimation in aorta included three cytosines located in the Kcns1 and Hsf4, explained R2 = 87.8% of the variation in age, and predicted age with an mean absolute error of 9.6 weeks in the independent test set. EAA in the aorta was associated with endothelial dysfunction in the abdominal aorta and femoral artery what was consistent with the EAA direction estimated in blood samples. The rate of vascular biological ageing in mice, reflected by the age-dependent systemic endothelial dysfunction, could be estimated using DNA methylation measurements at three loci in aorta and blood samples.

MCPIP1 Inhibits Hepatic Stellate Cell Activation in Autocrine and Paracrine Manners, Preventing Liver Fibrosis.

BACKGROUND & AIMS: Hepatic fibrosis is characterized by enhanced deposition of extracellular matrix (ECM), which results from the wound healing response to chronic, repeated injury of any etiology. Upon injury, hepatic stellate cells (HSCs) activate and secrete ECM proteins, forming scar tissue, which leads to liver dysfunction. Monocyte-chemoattractant protein-induced protein 1 (MCPIP1) possesses anti-inflammatory activity, and its overexpression reduces liver injury in septic mice. In addition, mice with liver-specific deletion of Zc3h12a develop features of primary biliary cholangitis. In this study, we investigated the role of MCPIP1 in liver fibrosis and HSC activation. METHODS: We analyzed MCPIP1 levels in patients' fibrotic livers and hepatic cells isolated from fibrotic murine livers. In vitro experiments were conducted on primary HSCs, cholangiocytes, hepatocytes, and LX-2 cells with MCPIP1 overexpression or silencing. RESULTS: MCPIP1 levels are induced in patients' fibrotic livers compared with their nonfibrotic counterparts. Murine models of fibrosis revealed that its level is increased in HSCs and hepatocytes. Moreover, hepatocytes with Mcpip1 deletion trigger HSC activation via the release of connective tissue growth factor. Overexpression of MCPIP1 in LX-2 cells inhibits their activation through the regulation of TGFB1 expression, and this phenotype is reversed upon MCPIP1 silencing. CONCLUSIONS: We demonstrated that MCPIP1 is induced in human fibrotic livers and regulates the activation of HSCs in both autocrine and paracrine manners. Our results indicate that MCPIP1 could have a potential role in the development of liver fibrosis.

Analysis of epigenetic clocks links yoga, sleep, education, reduced meat intake, coffee, and a SOCS2 gene variant to slower epigenetic aging.

DNA methylation (DNAm) clocks hold promise for measuring biological age, useful for guiding clinical interventions and forensic identification. This study compared the commonly used DNAm clocks, using DNA methylation and SNP data generated from nearly 1000 human blood or buccal swab samples. We evaluated different preprocessing methods for age estimation, investigated the association of epigenetic age acceleration (EAA) with various lifestyle and sociodemographic factors, and undertook a series of novel genome-wide association analyses for different EAA measures to find associated genetic variants. Our results highlighted the Skin&Blood clock with ssNoob normalization as the most accurate predictor of chronological age. We provided novel evidence for an association between the practice of yoga and a reduction in the pace of aging (DunedinPACE). Increased sleep and physical activity were associated with lower mortality risk score (MRS) in our dataset. University degree, vegetable consumption, and coffee intake were associated with reduced levels of epigenetic aging, whereas smoking, higher BMI, meat consumption, and manual occupation correlated well with faster epigenetic aging, with FitAge, GrimAge, and DunedinPACE clocks showing the most robust associations. In addition, we found a novel association signal for SOCS2 rs73218878 (p = 2.87 × 10-8) and accelerated GrimAge. Our study emphasizes the importance of an optimized DNAm analysis workflow for accurate estimation of epigenetic age, which may influence downstream analyses. The results support the influence of genetic background on EAA. The associated SOCS2 is a member of the suppressor of cytokine signaling family known for its role in human longevity. The reported association between various risk factors and EAA has practical implications for the development of health programs to improve quality of life and reduce premature mortality associated with age-related diseases.

miR-378 influences muscle satellite cells and enhances adipogenic potential of fibro-adipogenic progenitors but does not affect muscle regeneration in the glycerol-induced injury model.

Skeletal muscle regeneration relies on the reciprocal interaction between many types of cells. Regenerative capacity may be altered in different disorders. In our study, we investigated whether the deletion of miR-378a (miR-378) affects muscle regeneration. We subjected 6-week-old wild-type (WT) and miR-378 knockout (miR-378-/-) animals to the glycerol-induced muscle injury and performed analyses in various time-points. In miR-378-/- animals, an elevated abundance of muscle satellite cells (mSCs) on day 3 was found. Furthermore, fibro-adipogenic progenitors (FAPs) isolated from the muscle of miR-378-/- mice exhibited enhanced adipogenic potential. At the same time, lack of miR-378 did not affect inflammation, fibrosis, adipose tissue deposition, centrally nucleated fiber count, muscle fiber size, FAP abundance, and muscle contractility at any time point analyzed. To conclude, our study revealed that miR-378 deletion influences the abundance of mSCs and the adipogenic potential of FAPs, but does not affect overall regeneration upon acute, glycerol-induced muscle injury.

Introduction of a multiplex amplicon sequencing assay to quantify DNA methylation in target cytosine markers underlying four selected epigenetic clocks.

BACKGROUND: DNA methylation analysis has proven to be a powerful tool for age assessment. However, the implementation of epigenetic age prediction in diagnostics or routine forensic casework requires appropriate laboratory methods. In this study, we aimed to compare the performance of large-scale DNA methylation analysis protocols that show promise in terms of accuracy, throughput, multiplexing capacity, and high sensitivity. RESULTS: The protocols were designed to target a predefined panel of 161 genomic CG/CA sites from four known estimators of epigenetic age-related parameters, optimized and validated using artificially methylated controls or blood samples. We successfully targeted 96% of these loci using two enrichment protocols: Ion AmpliSeq™, an amplicon-based method integrated with Ion Torrent S5, and SureSelectXT Methyl-Seq, a hybridization-based method followed by MiSeq FGx sequencing. Both protocols demonstrated high accuracy and robustness. Although hybridization assays have greater multiplexing capabilities, the best overall performance was observed for the amplicon-based protocol with the lowest variability in DNA methylation at 25 ng of starting DNA, mean observed marker coverage of ~ 6.7 k reads, and accuracy of methylation quantification with a mean absolute difference between observed and expected methylation beta value of 0.054. The Ion AmpliSeq method correlated strongly with genome-scale EPIC microarray data (R = 0.91) and showed superiority in terms of methylation measurement accuracy. Method-to-method bias was accounted for by the use of linear transformation, which provided a highly accurate prediction of calendar age with a mean absolute error of less than 5 years for the VISAGE and Hannum age clocks used. The pace of aging (PoAm) and the mortality risk score (MRS) estimators included in our panel represent next-generation clocks, were found to have low to moderate correlations with the VISAGE and Hannum models (R < 0.75), and thus may capture different aspects of epigenetic aging. CONCLUSIONS: We propose a laboratory tool that allows the quantification of DNA methylation in cytosines underlying four different clocks, thus providing broad information on epigenetic aging while maintaining a reasonable number of CpG markers, opening the way to a wide range of applications in forensics, medicine, and healthcare.

DNAmFitAge: biological age indicator incorporating physical fitness.

Physical fitness is a well-known correlate of health and the aging process and DNA methylation (DNAm) data can capture aging via epigenetic clocks. However, current epigenetic clocks did not yet use measures of mobility, strength, lung, or endurance fitness in their construction. We develop blood-based DNAm biomarkers for fitness parameters gait speed (walking speed), maximum handgrip strength, forced expiratory volume in one second (FEV1), and maximal oxygen uptake (VO2max) which have modest correlation with fitness parameters in five large-scale validation datasets (average r between 0.16-0.48). We then use these DNAm fitness parameter biomarkers with DNAmGrimAge, a DNAm mortality risk estimate, to construct DNAmFitAge, a new biological age indicator that incorporates physical fitness. DNAmFitAge is associated with low-intermediate physical activity levels across validation datasets (p = 6.4E-13), and younger/fitter DNAmFitAge corresponds to stronger DNAm fitness parameters in both males and females. DNAmFitAge is lower (p = 0.046) and DNAmVO2max is higher (p = 0.023) in male body builders compared to controls. Physically fit people have a younger DNAmFitAge and experience better age-related outcomes: lower mortality risk (p = 7.2E-51), coronary heart disease risk (p = 2.6E-8), and increased disease-free status (p = 1.1E-7). These new DNAm biomarkers provide researchers a new method to incorporate physical fitness into epigenetic clocks.

Overlapping association signals in the genetics of hair-related phenotypes in humans and their relevance to predictive DNA analysis.

Genetic prediction of different hair phenotypes can help reconstruct the physical appearance of an individual whose biological sample is analyzed in criminal and identification cases. Up to date, forensic prediction models for hair colour, hair shape, hair loss and hair greying have been developed, but studies investigating predictability of hair thickness and density traits are missing. First data suggesting overlapping associations in various hair features have emerged in recent years, suggesting partially common genetic basis and molecular mechanisms, and this knowledge can be used for predictive purposes. Here we aim to broaden our understanding of the genetics underlying head, facial and body hair thickness and density traits and examine the association for a set of literature SNPs. We characterize the overlap in SNP association for various hair phenotypes, the extent of genetic interactions and the potential for genetic prediction. The study involved 999 samples from Poland, genotyped for 240 SNPs with targeted next-generation sequencing. Logistic regression methods were applied for association and prediction analyses while entropy-based approach was used for interaction testing. As a result, we refined known associations for monobrow and hairiness (PAX3, 5q13.2, TBX) and identified two novel association signals in IGFBP5 and VDR. Both genes were among top significant loci, showed broad association with different hair-related traits and were implicated in multiple interaction effects. Overall, for 14.7% of SNPs previously associated with head hair loss and/or hair shape, a positive signal of association was revealed with at least one hair feature studied in the current research. Overlap in association with at least two hair-related traits was demonstrated for 24 distinct loci. We showed that the associated SNPs explain ∼5-30% of the variation observed in particular hair traits and allow moderate accuracy of prediction. The highest accuracy was achieved for hairiness level prediction in females (AUC = 0.69 for the "none", 0.69 for the "low" and 0.76 for the "excessive" hairiness category) and monobrow (AUC = 0.69 for the "none", 0.62 for the "slight" and 0.70 for the "significant" monobrow category) with 33% of the variation in hairiness level in females explained by 7 SNPs and age, and 20% of the variation in monobrow captured by 7 SNPs and sex. Our study presents clear evidence of pleiotropy and epistasis in the genetics of hair traits. The acquired knowledge may have practical application in forensics, as well as in the cosmetic industry and anthropological research.

miR-378 affects metabolic disturbances in the mdx model of Duchenne muscular dystrophy.

Although Duchenne muscular dystrophy (DMD) primarily affects muscle tissues, the alterations to systemic metabolism manifested in DMD patients contribute to the severe phenotype of this fatal disorder. We propose that microRNA-378a (miR-378) alters carbohydrate and lipid metabolism in dystrophic mdx mice. In our study, we utilized double knockout animals which lacked both dystrophin and miR-378 (mdx/miR-378-/-). RNA sequencing of the liver identified 561 and 194 differentially expressed genes that distinguished mdx versus wild-type (WT) and mdx/miR-378-/- versus mdx counterparts, respectively. Bioinformatics analysis predicted, among others, carbohydrate metabolism disorder in dystrophic mice, as functionally proven by impaired glucose tolerance and insulin sensitivity. The lack of miR-378 in mdx animals mitigated those effects with a faster glucose clearance in a glucose tolerance test (GTT) and normalization of liver glycogen levels. The absence of miR-378 also restored the expression of genes regulating lipid homeostasis, such as Acly, Fasn, Gpam, Pnpla3, and Scd1. In conclusion, we report for the first time that miR-378 loss results in increased systemic metabolism of mdx mice. Together with our previous finding, demonstrating alleviation of the muscle-related symptoms of DMD, we propose that the inhibition of miR-378 may represent a new strategy to attenuate the multifaceted symptoms of DMD.

Predicting Physical Appearance from DNA Data-Towards Genomic Solutions.

The idea of forensic DNA intelligence is to extract from genomic data any information that can help guide the investigation. The clues to the externally visible phenotype are of particular practical importance. The high heritability of the physical phenotype suggests that genetic data can be easily predicted, but this has only become possible with less polygenic traits. The forensic community has developed DNA-based predictive tools by employing a limited number of the most important markers analysed with targeted massive parallel sequencing. The complexity of the genetics of many other appearance phenotypes requires big data coupled with sophisticated machine learning methods to develop accurate genomic predictors. A significant challenge in developing universal genomic predictive methods will be the collection of sufficiently large data sets. These should be created using whole-genome sequencing technology to enable the identification of rare DNA variants implicated in phenotype determination. It is worth noting that the correctness of the forensic sketch generated from the DNA data depends on the inclusion of an age factor. This, however, can be predicted by analysing epigenetic data. An important limitation preventing whole-genome approaches from being commonly used in forensics is the slow progress in the development and implementation of high-throughput, low DNA input sequencing technologies. The example of palaeoanthropology suggests that such methods may possibly be developed in forensics.

A collaborative exercise on DNA methylation-based age prediction and body fluid typing.

DNA methylation has become one of the most useful biomarkers for age prediction and body fluid identification in the forensic field. Therefore, several assays have been developed to detect age-associated and body fluid-specific DNA methylation changes. Among the many methods developed, SNaPshot-based assays should be particularly useful in forensic laboratories, as they permit multiplex analysis and use the same capillary electrophoresis instrumentation as STR analysis. However, technical validation of any developed assays is crucial for their proper integration into routine forensic workflow. In the present collaborative exercise, two SNaPshot multiplex assays for age prediction and a SNaPshot multiplex for body fluid identification were tested in twelve laboratories. The experimental set-up of the exercise was designed to reflect the entire workflow of SNaPshot-based methylation analysis and involved four increasingly complex tasks designed to detect potential factors influencing methylation measurements. The results of body fluid identification from each laboratory provided sufficient information to determine appropriate age prediction methods in subsequent analysis. In age prediction, systematic measurement differences resulting from the type of genetic analyzer used were identified as the biggest cause of DNA methylation variation between laboratories. Also, the use of a buffer that ensures a high ratio of specific to non-specific primer binding resulted in changes in DNA methylation measurement, especially when using degenerate primers in the PCR reaction. In addition, high input volumes of bisulfite-converted DNA often caused PCR failure, presumably due to carry-over of PCR inhibitors from the bisulfite conversion reaction. The proficiency of the analysts and experimental conditions for efficient SNaPshot reactions were also important for consistent DNA methylation measurement. Several bisulfite conversion kits were used for this study, but differences resulting from the use of any specific kit were not clearly discerned. Even when different experimental settings were used in each laboratory, a positive outcome of the study was a mean absolute age prediction error amongst participant's data of only 2.7 years for semen, 5.0 years for blood and 3.8 years for saliva.

MCPIP1 inhibits Wnt/ß-catenin signaling pathway activity and modulates epithelial-mesenchymal transition during clear cell renal cell carcinoma progression by targeting miRNAs.

Epithelial-mesenchymal transition (EMT) refers to the acquisition of mesenchymal properties in cells participating in tumor progression. One hallmark of EMT is the increased level of active ß-catenin, which can trigger the transcription of Wnt-specific genes responsible for the control of cell fate. We investigated how Monocyte Chemotactic Protein-1-Induced Protein-1 (MCPIP1), a negative regulator of inflammatory processes, affects EMT in a clear cell renal cell carcinoma (ccRCC) cell line, patient tumor tissues and a xenotransplant model. We showed that MCPIP1 degrades miRNAs via its RNase activity and thus protects the mRNA transcripts of negative regulators of the Wnt/ß-catenin pathway from degradation, which in turn prevents EMT. Mechanistically, the loss of MCPIP1 RNase activity led to the upregulation of miRNA-519a-3p, miRNA-519b-3p, and miRNA-520c-3p, which inhibited the expression of Wnt pathway inhibitors (SFRP4, KREMEN1, CXXC4, CSNK1A1 and ZNFR3). Thus, the level of active nuclear ß-catenin was increased, leading to increased levels of EMT inducers (SNAI1, SNAI2, ZEB1 and TWIST) and, consequently, decreased expression of E-cadherin, increased expression of mesenchymal markers, and acquisition of the mesenchymal phenotype. This study revealed that MCPIP1 may act as a tumor suppressor that prevents EMT by stabilizing Wnt inhibitors and decreasing the levels of active ß-catenin and EMT inducers.

Impact of excessive alcohol abuse on age prediction using the VISAGE enhanced tool for epigenetic age estimation in blood.

DNA methylation-based clocks provide the most accurate age estimates with practical implications for clinical and forensic genetics. However, the effects of external factors that may influence the estimates are poorly studied. Here, we evaluated the effect of alcohol consumption on epigenetic age prediction in a cohort of extreme alcohol abusers. Blood samples from deceased alcohol abusers and age- and sex-matched controls were analyzed using the VISAGE enhanced tool for age prediction from somatic tissues that enables examination of 44 CpGs within eight age markers. Significantly altered DNA methylation was recorded for alcohol abusers in MIR29B2CHG. This resulted in a mean predicted age of 1.4 years higher compared to the controls and this trend increased in older individuals. The association of alcohol abuse with epigenetic age acceleration, as determined by the prediction analysis performed based on MIR29B2CHG, was small but significant (ß = 0.190; P-value = 0.007). However, the observed alteration in DNA methylation of MIR29B2CHG had a non-significant effect on age estimation with the VISAGE age prediction model. The mean absolute error in the alcohol-abusing cohort was 3.1 years, compared to 3.3 years in the control group. At the same time, upregulation of MIR29B2CHG expression may have a biological function, which merits further studies.

Epigenetic age prediction in semen - marker selection and model development.

DNA methylation analysis is becoming increasingly useful in biomedical research and forensic practice. The discovery of differentially methylated sites (DMSs) that continuously change over an individual's lifetime has led to breakthroughs in molecular age estimation. Although semen samples are often used in forensic DNA analysis, previous epigenetic age prediction studies mainly focused on somatic cell types. Here, Infinium MethylationEPIC BeadChip arrays were applied to semen-derived DNA samples, which identified numerous novel DMSs moderately correlated with age. Validation of the ten most age-correlated novel DMSs and three previously known sites in an independent set of semen-derived DNA samples using targeted bisulfite massively parallel sequencing, confirmed age-correlation for nine new and three previously known markers. Prediction modelling revealed the best model for semen, based on 6 CpGs from newly identified genes SH2B2, EXOC3, IFITM2, and GALR2 as well as the previously known FOLH1B gene, which predict age with a mean absolute error of 5.1 years in an independent test set. Further increases in the accuracy of age prediction from semen DNA will require technological progress to allow sensitive, simultaneous analysis of a much larger number of age correlated DMSs from the compromised DNA typical of forensic semen stains.

Searching for improvements in predicting human eye colour from DNA.

Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

Role of Heme-Oxygenase-1 in Biology of Cardiomyocytes Derived from Human Induced Pluripotent Stem Cells.

Heme oxygenase-1 (HO-1, encoded by HMOX1) is a cytoprotective enzyme degrading heme into CO, Fe2+, and biliverdin. HO-1 was demonstrated to affect cardiac differentiation of murine pluripotent stem cells (PSCs), regulate the metabolism of murine adult cardiomyocytes, and influence regeneration of infarcted myocardium in mice. However, the enzyme's effect on human cardiogenesis and human cardiomyocytes' electromechanical properties has not been described so far. Thus, this study aimed to investigate the role of HO-1 in the differentiation of human induced pluripotent stem cells (hiPSCs) into hiPSC-derived cardiomyocytes (hiPSC-CMs). hiPSCs were generated from human fibroblasts and peripheral blood mononuclear cells using Sendai vectors and subjected to CRISPR/Cas9-mediated HMOX1 knock-out. After confirming lack of HO-1 expression on the protein level, isogenic control and HO-1-deficient hiPSCs were differentiated into hiPSC-CMs. No differences in differentiation efficiency and hiPSC-CMs metabolism were observed in both cell types. The global transcriptomic analysis revealed, on the other hand, alterations in electrophysiological pathways in hiPSC-CMs devoid of HO-1, which also demonstrated increased size. Functional consequences in changes in expression of ion channels genes were then confirmed by patch-clamp analysis. To the best of our knowledge, this is the first report demonstrating the link between HO-1 and electrophysiology in human cardiomyocytes.

Development of the VISAGE enhanced tool and statistical models for epigenetic age estimation in blood, buccal cells and bones.

DNA methylation is known as a biomarker for age with applications in forensics. Here we describe the VISAGE (VISible Attributes through GEnomics) Consortium's enhanced tool for epigenetic age estimation in somatic tissues. The tool is based on eight DNA methylation markers (44 CpGs), bisulfite multiplex PCR followed by sequencing on the MiSeq FGx platform, and three statistical prediction models for blood, buccal cells and bones. The model for blood is based on six CpGs from ELOVL2, MIR29B2CHG, KLF14, FHL2, TRIM59 and PDE4C, and predicts age with a mean absolute error (MAE) of 3.2 years, while the model for buccal cells includes five CpGs from PDE4C, MIR29B2CHG, ELOVL2, KLF14 and EDARADD and predicts age with MAE of 3.7 years, and the model for bones has six CpGs from ELOVL2, KLF14, PDE4C and ASPA and predicts age with MAE of 3.4 years. The VISAGE enhanced tool for age estimation in somatic tissues enables reliable collection of DNA methylation data from small amounts of DNA using a sensitive multiplex MPS assay that provides accurate estimation of age in blood, buccal swabs, and bones using the statistical model tailored to each tissue.

DNA methylation-based age clocks: From age prediction to age reversion.

Aging as an irretrievable occurrence throughout the entire life is characterized by a progressive decline in physiological functionality and enhanced disease vulnerability. Numerous studies have demonstrated that epigenetic modifications, particularly DNA methylation (DNAm), correlate with aging and age-related diseases. Several investigations have attempted to predict chronological age using the age-related alterations in the DNAm of certain CpG sites. Here we categorize different studies that tracked the aging process in the DNAm landscape to show how epigenetic age clocks evolved from a chronological age estimator to an indicator of lifespan and healthspan. We also describe the health and disease predictive potential of estimated epigenetic age acceleration regarding different clinical conditions and lifestyle factors. Considering the revealed age-related epigenetic changes, the recent age-reprogramming strategies are discussed which are promising methods for resetting the aging clocks.

Deletion of Mcpip1 in Mcpip1fl/flAlbCre mice recapitulates the phenotype of human primary biliary cholangitis.

Primary biliary cholangitis (PBC) is an autoimmune disease characterized by progressive destruction of the intrahepatic bile ducts. The immunopathology of PBC involves excessive inflammation; therefore, negative regulators of inflammatory response, such as Monocyte Chemoattractant Protein-1-Induced Protein-1 (MCPIP1) may play important roles in the development of PBC. The aim of this work was to verify whether Mcpip1 expression protects against development of PBC. Genetic deletion of Zc3h12a was used to characterize the role of Mcpip1 in the pathogenesis of PBC in 6-52-week-old mice. We found that Mcpip1 deficiency in the liver (Mcpip1fl/flAlbCre) recapitulates most of the features of human PBC, in contrast to mice with Mcpip1 deficiency in myeloid cells (Mcpip1fl/flLysMCre mice), which present with robust myeloid cell-driven systemic inflammation. In Mcpip1fl/flAlbCre livers, intrahepatic bile ducts displayed proliferative changes with inflammatory infiltration, bile duct destruction, and fibrosis leading to cholestasis. In plasma, increased concentrations of IgG, IgM, and AMA autoantibodies (anti-PDC-E2) were detected. Interestingly, the phenotype of Mcpip1fl/flAlbCre mice was robust in 6-week-old, but milder in 12-24-week-old mice. Hepatic transcriptome analysis of 6-week-old and 24-week-old Mcpip1fl/flAlbCre mice showed 812 and 8 differentially expressed genes, respectively, compared with age-matched control mice, and revealed a distinct set of genes compared to those previously associated with development of PBC. In conclusion, Mcpip1fl/flAlbCre mice display early postnatal phenotype that recapitulates most of the features of human PBC.

Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits.

The prediction of appearance traits by use of solely genetic information has become an established approach and a number of statistical prediction models have already been developed for this purpose. However, given limited knowledge on appearance genetics, currently available models are incomplete and do not include all causal genetic variants as predictors. Therefore such prediction models may benefit from the inclusion of additional information that acts as a proxy for this unknown genetic background. Use of priors, possibly informed by trait category prevalence values in biogeographic ancestry groups, in a Bayesian framework may thus improve the prediction accuracy of previously predicted externally visible characteristics, but has not been investigated as of yet. In this study, we assessed the impact of using trait prevalence-informed priors on the prediction performance in Bayesian models for eye, hair and skin color as well as hair structure and freckles in comparison to the respective prior-free models. Those prior-free models were either similarly defined either very close to the already established ones by using a reduced predictive marker set. However, these differences in the number of the predictive markers should not affect significantly our main outcomes. We observed that such priors often had a strong effect on the prediction performance, but to varying degrees between different traits and also different trait categories, with some categories barely showing an effect. While we found potential for improving the prediction accuracy of many of the appearance trait categories tested by using priors, our analyses also showed that misspecification of those prior values often severely diminished the accuracy compared to the respective prior-free approach. This emphasizes the importance of accurate specification of prevalence-informed priors in Bayesian prediction modeling of appearance traits. However, the existing literature knowledge on spatial prevalence is sparse for most appearance traits, including those investigated here. Due to the limitations in appearance trait prevalence knowledge, our results render the use of trait prevalence-informed priors in DNA-based appearance trait prediction currently infeasible.

Exploring the possibility of predicting human head hair greying from DNA using whole-exome and targeted NGS data.

BACKGROUND: Greying of the hair is an obvious sign of human aging. In addition to age, sex- and ancestry-specific patterns of hair greying are also observed and the progression of greying may be affected by environmental factors. However, little is known about the genetic control of this process. This study aimed to assess the potential of genetic data to predict hair greying in a population of nearly 1000 individuals from Poland. RESULTS: The study involved whole-exome sequencing followed by targeted analysis of 378 exome-wide and literature-based selected SNPs. For the selection of predictors, the minimum redundancy maximum relevance (mRMRe) method was used, and then two prediction models were developed. The models included age, sex and 13 unique SNPs. Two SNPs of the highest mRMRe score included whole-exome identified KIF1A rs59733750 and previously linked with hair loss FGF5 rs7680591. The model for greying vs. no greying prediction achieved accuracy of cross-validated AUC = 0.873. In the 3-grade classification cross-validated AUC equalled 0.864 for no greying, 0.791 for mild greying and 0.875 for severe greying. Although these values present fairly accurate prediction, most of the prediction information was brought by age alone. Genetic variants explained < 10% of hair greying variation and the impact of particular SNPs on prediction accuracy was found to be small. CONCLUSIONS: The rate of changes in human progressive traits shows inter-individual variation, therefore they are perceived as biomarkers of the biological age of the organism. The knowledge on the mechanisms underlying phenotypic aging can be of special interest to the medicine, cosmetics industry and forensics. Our study improves the knowledge on the genetics underlying hair greying processes, presents prototype models for prediction and proves hair greying being genetically a very complex trait. Finally, we propose a four-step approach based on genetic and epigenetic data analysis allowing for i) sex determination; ii) genetic ancestry inference; iii) greying-associated SNPs assignment and iv) epigenetic age estimation, all needed for a final prediction of greying.