Genome-wide identification of age-related CpG sites for age estimation from blood DNA of Han Chinese individuals

Elucidating genetic and molecular basis of altered higher-order brain structure-function coupling in major depressive disorder

Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/β-hydrolase domain-containing 6 (ABHD6), β 1,3-N-acetylglucosaminyltransferase-9(β3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, β3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Improved age estimation from semen using sperm-specific age-related CpG markers

Accurate age estimation from semen has the potential to greatly narrow the pool of unidentified suspects in sexual assault investigations. However, previous efforts utilizing semen age-related CpG (AR-CpG) markers have shown lower accuracy compared to blood AR-CpG-based methods. This discrepancy may be attributed to DNA methylation (DNAm) interferences from "round cells" such as leukocytes and immature sperm cells in semen. This study aimed to develop age calculators based on sperm-specific AR-CpG markers and to achieve performance-improved age estimates from sperm DNA. Through an analysis of publicly available MethylationEPIC microarray data from 90 sperm samples of healthy males aged 22-51 years, we identified 31 sperm-specific AR-CpG markers with absolute Pearson's R values > 0.5 and Benjamini-Hochberg adjusted p values < 0.013. The top 19 AR-CpG markers with the largest absolute R values and beta ranges > 0.10, along with 3 reported semen AR-CpG markers (cg06304190, cg06979108, and cg12837463), were integrated into two methylation SNaPshot panels (Ⅰ and Ⅱ), each containing 11 markers. The 21 qualified AR-CpG markers showed absolute R values ≥ 0.427 in an independent validation cohort of 253 sperm DNA samples (22-67 years), with cg21843517 exhibiting the strongest age correlation (R = 0.853). The optimal models, constructed using sperm DNAm data of the training set (n = 214, 22-67 years) and markers from panel Ⅰ (n = 11), panel Ⅱ (n = 10), or both panels, achieved mean absolute errors (MAEs) of 2.526-4.746, 3.890-5.715, and > 9.800 years on the test sets of sperm (n = 39, 23-64 years), semen (same donors as the sperm test set), and whole blood (n = 40, 22-65 years), respectively. The simplified models incorporating 3, 5, 9, or 14 AR-CpG markers (MAE = 2.918-4.139 years for sperm) still outperformed the Lee et al. original model (MAE = 6.444 years for semen) and the reconstructed panel Lee model (MAE = 6.011 years for sperm). The final models, utilizing all sperm DNAm data (n = 253) and markers from panel Ⅰ, panel Ⅱ, or both panels, yielded mean MAEs of 2.587, 2.766, and 2.200 years, respectively, on the 50 test sets generated by 5 repeats of 10-fold cross-validations. Additionally, multiple markers in both panels demonstrated the ability to discern sperm or semen from blood with 100% accuracy. In summary, our study substantiates the potential of sperm-specific AR-CpG markers for precise age estimation from sperm DNA, providing an improved toolset for forensic investigations.

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm

Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.

β3GNT9 as a prognostic biomarker in glioblastoma and its association with glioblastoma immune infiltration, migration and invasion

Background

Studies have shown that the immune infiltration of tumor microenvironment is related to the prognosis of glioblastoma, which is characterized by high heterogeneity, high recurrence rate and low survival rate. To unravel the role of β1,3-N-acetylglucosaminyltransferase-9 (β3GNT9) in the progression of glioblastoma, this study identifies the value of β3GNT9 as a prognostic biomarker in glioblastoma, and investigates the relationship between β3GNT9 expression and glioblastoma immune infiltration, migration and invasion.

Methods

β3GNT9 expression in glioblastoma was analyzed using the GEPIA database. The clinical features of glioblastoma were screened out from the TCGA database. The relationship between β3GNT9 expression and clinical features was analyzed. The relationship between β3GNT9 and the prognosis of glioblastoma was evaluated through univariate and multivariate COX regression analyses, and the survival analysis was conducted using the Kaplan-Meier method. GSEA was employed to predict the signaling pathway of β3GNT9 in glioblastoma. The correlation between β3GNT9 and tumor immune infiltration was analyzed using the related modules of CIBERSORT and TIMER. A172, U87MG and U251 cell lines were selected to verify β3GNT9 expression in vitro. The effects of β3GNT9 on the migration and invasion of glioblastoma were investigated through cell scratch and invasion assays.

Results

β3GNT9 expression in glioblastoma group was significantly higher than that in normal brain tissue group (P<0.05). The overall survival rate in high β3GNT9 expression group was significantly lower than that in low β3GNT9 expression group (P<0.05). Regression analyses suggested that β3GNT9, involved primarily in glucosamine degradation and extracellular matrix receptor interaction, could be an independent prognostic factor for glioblastoma. CIBERSORT and GEPIA database analyses showed that β3GNT9 was correlated with tumor infiltrating immune cells such as T follicular helper cells, activating natural killer cells, monocytes, macrophages, and eosinophils, thus affecting the immune microenvironment of glioblastoma. Cell experiments confirmed that β3GNT9 was highly expressed in A172, U87MG and U251 cell lines (P<0.05), and promoted the migration and invasion of glioblastoma (P<0.05).

Conclusion

The increased expression of β3GNT9 in glioblastoma can affect the immune microenvironment of glioblastoma and promote its migration and invasion. β3GNT9 can be used as a potential independent prognostic biomarker for patients with glioblastoma.

Prediction of chronological age and its applications in forensic casework: methods, current practices, and future perspectives

Estimating an individual's age can be relevant in several areas primarily related to the clinical and forensic fields. In the latter, estimation of an individual's chronological age from biological material left by the perpetrator at a crime scene may provide helpful information for police investigation. Estimation of age is also beneficial in immigration cases, where age can affect the person's protection status under the law, or in disaster victim identification to narrow the list of potential missing persons. In the last decade, research has focused on establishing new approaches for age prediction in the forensic field. From the first forensic age estimations based on morphological inspections of macroscopic changes in bone and teeth, the focus has shifted to molecular methods for age estimation. These methods allow the use of samples from human biological material that does not contain morphological age features and can, in theory, be investigated in traces containing only small amounts of biological material. Molecular methods involving DNA analyses are the primary choice and estimation of DNA methylation levels at specific sites in the genome is the most promising tool. This review aims to provide an overview of the status of forensic age prediction using molecular methods, with particular focus in DNA methylation. The frequent challenges that impact forensic age prediction model development will be addressed, together with the importance of validation efforts within the forensic community.

Accurate age estimation from blood samples of Han Chinese individuals using eight high-performance age-related CpG sites

Age-related CpG sites (AR-CpGs) are currently the most promising biomarkers for forensic age estimation. In our previous studies, we first validated the age correlation of seven reported AR-CpGs in blood samples of Chinese Han population. Subsequently, we screened some good age predictors from blood samples of Chinese Han population, and built pyrosequencing-based age prediction models. However, it is still important to select a set of high-performance AR-CpGs in a specific racial group and establish a simple and efficient method for accurate age estimation for forensic purpose. In this study, eight AR-CpGs, namely chr6: 11,044,628 (ELOVL2), cg06639320 (FHL2), chr1: 207,823,723 (C1orf132), cg19283806 (CCDC102B), cg14361627 (KLF14), cg17740900 (SYNE2), cg07553761 (TRIM59), and cg26947034, were selected based on our previous studies, and a multiplex methylation SNaPshot assay was developed to investigate DNA methylation levels at these AR-CpGs in 529 blood samples (aged 2-82 years) from Han Chinese population. All selected CpG sites showed strong age correlation with the correlation coefficient (r) from 0.8363 to 0.9251. Multiple linear regression (MLR) and support vector regression (SVR) age prediction models were simultaneously established to fit change characteristics of DNA methylation levels of eight AR-CpGs with the age in 374 donors' blood samples. The MLR model enabled age prediction with R² = 0.923, mean absolute error (MAE) = 3.52, while the SVR model enabled age prediction with R² = 0.935, MAE = 2.88. One hundred fifty-five independent samples were used as a validation set to test the two models' performance, and the prediction MAE for the validation set was 3.71 and 3.34 for the MLR and SVR models, respectively. For the MLR and SVR models, the correct prediction rate at ± 5 years reached a high level of 79.35% and 83.23%, respectively. In general, these statistical parameters indicated that the SVR model outperformed the MLR model in age prediction of the Han Chinese population. In addition, our method provides sufficient sensitivity in forensic applications and allows for 100% efficiency when examining bloodstains kept in room conditions for up to 43 days. These results indicate that our multiplex methylation SNaPshot assay is a reliable, effective, and accurate method for age prediction in blood samples from the Chinese Han population.

A cautionary note on altered pace of aging in the COVID-19 era

Coronavirus disease 2019 (COVID-19) is highly age-dependent due to hi-jacking the molecular control of the immune cells by the severe acute respiratory syndrome-corona virus 2 (SARS-CoV-2) leading to aberrant DNA methylation (DNAm) pattern of blood in comparison to normal individuals. These epigenetic modifications have been linked to perturbations to the epigenetic clock, development of long COVID-19 syndrome, and all-cause mortality risk. I reviewed the effects of COVID-19 on different molecular age markers such as the DNAm, telomere length (TL), and signal joint T-cell receptor excision circle (sjTREC). Integrating the accumulated clinical research data, COVID-19 and novel medical management may alter the pace of aging in adult individuals (<60 years). As such, COVID-19 might be a confounder in epigenetic age estimation similar to life style diversities, pathogens and pathologies which may influence the interpretation of DNAm data. Similarly, the SARS-CoV-2 affects T-lymphocyte function with possible influence on sjTREC levels. In contrast, TL measurements performed years before the SARS-CoV-2 pandemic proved that short TL predisposes to severe COVID- 19 independently from chronological age. However, the persistence of COVID-19 epigenetic scars and the durability of the immune response after vaccination and their effect on the ongoing pace of aging are still unknown. In the light of these data, the heterogeneous nature of the samples in these studies mandates a systematic evaluation of the currrent methods. SARS-CoV-2 may modify the reliability of the age estimation models in real casework because blood is the most common biological sample encountered in forensic contexts.

Accelerated biological aging in COVID-19 patients

Chronological age is a risk factor for SARS-CoV-2 infection and severe COVID-19. Previous findings indicate that epigenetic age could be altered in viral infection. However, the epigenetic aging in COVID-19 has not been well studied. In this study, DNA methylation of the blood samples from 232 healthy individuals and 413 COVID-19 patients is profiled using EPIC methylation array. Epigenetic ages of each individual are determined by applying epigenetic clocks and telomere length estimator to the methylation profile of the individual. Epigenetic age acceleration is calculated and compared between groups. We observe strong correlations between the epigenetic clocks and individual's chronological age (r > 0.8, p < 0.0001). We also find the increasing acceleration of epigenetic aging and telomere attrition in the sequential blood samples from healthy individuals and infected patients developing non-severe and severe COVID-19. In addition, the longitudinal DNA methylation profiling analysis find that the accumulation of epigenetic aging from COVID-19 syndrome could be partly reversed at late clinic phases in some patients. In conclusion, accelerated epigenetic aging is associated with the risk of SARS-CoV-2 infection and developing severe COVID-19. In addition, the accumulation of epigenetic aging from COVID-19 may contribute to the post-COVID-19 syndrome among survivors.

Chronological Age Prediction: Developmental Evaluation of DNA Methylation-Based Machine Learning Models

Epigenetic clock, a highly accurate age estimator based on DNA methylation (DNAm) level, is the basis for predicting mortality/morbidity and elucidating the molecular mechanism of aging, which is of great significance in forensics, justice, and social life. Herein, we integrated machine learning (ML) algorithms to construct blood epigenetic clock in Southern Han Chinese (CHS) for chronological age prediction. The correlation coefficient (r) meta-analyses of 7,084 individuals were firstly implemented to select five genes (ELOVL2, C1orf132, TRIM59, FHL2, and KLF14) from a candidate set of nine age-associated DNAm biomarkers. The DNAm-based profiles of the CHS cohort (240 blood samples differing in age from 1 to 81 years) were generated by the bisulfite targeted amplicon pyrosequencing (BTA-pseq) from 34 cytosine-phosphate-guanine sites (CpGs) of five selected genes, revealing that the methylation levels at different CpGs exhibit population specificity. Furthermore, we established and evaluated four chronological age prediction models using distinct ML algorithms: stepwise regression (SR), support vector regression (SVR-eps and SVR-nu), and random forest regression (RFR). The median absolute deviation (MAD) values increased with chronological age, especially in the 61–81 age category. No apparent gender effect was found in different ML models of the CHS cohort (all p &gt; 0.05). The MAD values were 2.97, 2.22, 2.19, and 1.29 years for SR, SVR-eps, SVR-nu, and RFR in the CHS cohort, respectively. Eventually, compared to the MAD range of the meta cohort (2.53–5.07 years), a promising RFR model (ntree = 500 and mtry = 8) was optimized with an MAD of 1.15 years in the 1–60 age categories of the CHS cohort, which could be regarded as a robust epigenetic clock in blood for age-related issues.