Age determination through DNA methylation patterns in fingernails and toenails

Over the past decade, age prediction based on DNA methylation has become a vastly investigated topic; many age prediction models have been developed based on different DNAm markers and using various tissues. However, the potential of using nails to this end has not yet been explored. Their inherent resistance to decay and ease of sampling would offer an advantage in cases where post-mortem degradation poses challenges concerning sample collection and DNA-extraction. In the current study, clippings from both fingernails and toenails were collected from 108 living test subjects (age range: 0–96 years). The methylation status of 15 CpGs located in 4 previously established age-related markers (ASPA, EDARADD, PDE4C, ELOVL2) was investigated through pyrosequencing of bisulphite converted DNA. Significant dissimilarities in methylation levels were observed between all four limbs, hence both limb-specific age prediction models and prediction models combining multiple sampling locations were developed. When applied to their respective test sets, these models yielded a mean absolute deviation between predicted and chronological age ranging from 5.48 to 9.36 years when using ordinary least squares regression. In addition, the assay was tested on methylation data derived from 5 nail samples collected from deceased individuals, demonstrating its feasibility for application in post-mortem cases. In conclusion, this study provides the first proof that chronological age can be assessed through DNA methylation patterns in nails.     

Risk factors for acute rejection in liver transplantation and its impact on the outcomes of recipients

ObjectiveTo determine the risk factors for acute rejection in liver transplantation and its impact on the outcomes of the recipients.MethodsClinicopathological data of 290 patients who underwent liver transplantation from January 2012 to December 2021 at our center were retrospectively evaluated. Patients were grouped into an acute rejection (AR) group and a normal (NM) group based on the confirmed histopathological diagnosis of acute rejection. Univariate and multivariate logistic regression were used to determine the risk factors for acute rejection.Results244 patients were included in the study. Acute rejection occurred in 27 (11.1%) of the patients. Warm ischemia time (P = 0.137), cold ischemia time (P = 0.064) and chronic liver failure (P = 0.001) were potential risk factors for acute rejection. Chronic liver failure (P < 0.001, OR = 8.22, 95% CI = 2.47–27.32) was the independent risk factor. There was no significant difference in overall survival between recipients with acute rejection and those without it (P = 0.985). The 1-, 3- and 5-year overall survival in the NM group was 98.1%, 85.7% and 78.6% respectively vs 88.9%, 82.5% and 82.5% respectively in the AR group.ConclusionAcute rejection does not appear to affect the long-term survival of the recipients. Only chronic liver failure was an independent risk factor for acute rejection. Our findings further illustrate that contradictions still exist on which factors influence acute rejection in liver transplant recipients.SummaryClinicopathological data of 290 liver transplant recipients at our center between January 2012 and December 2021 were retrospectively evaluated to determine the risk factors for acute rejection and its impact on the outcomes of the recipients. 244 patients were included in the analysis. 27 of the 244 experienced acute rejection. Propensity score matching was performed to reduce the confounding effect. Patients were assigned to an acute rejection group (n = 27) and a normal group (n = 54). Chronic liver failure (P < 0.001, OR = 8.22, 95% CI = 2.47–27.32) was the determined to be independent risk factor for acute rejection. Acute rejection did not appear to affect the long-term survival of the recipients and there was no significant difference in overall survival between the patients with acute rejection and those without it.     

Tracing recent outdoor geolocation by analyzing microbiota from shoe soles and shoeprints even after indoor walking

The microbial communities on shoe soles and shoeprints could carry microbial information about where someone walked. This is possible evidence to link a suspect in a crime case to a geographic location. A previous study had shown that the microbiota found on shoe soles depend on the microbiota of the soil on which people walk. However, there is a turnover of microbial communities on shoe soles during walking. The impact of microbial community turnover on tracing recent geolocation from shoe soles has not been adequately studied. In addition, it is still unclear whether the microbiota of shoeprints can be used to determine recent geolocation. In this preliminary study, we investigated whether the microbial characteristics of shoe soles and shoeprints can be used to trace geolocation and whether this information can be destroyed by walking on indoor floors. In this study, participants were asked to walk outdoors on exposed soil, then walk indoors on a hard wood floor. High-throughput sequencing of the 16S rRNA gene was performed to characterize the microbial communities of shoe soles, shoeprints, indoor dust, and outdoor soil. Samples of shoe soles and shoeprints were collected at steps 5, 20, and 50 while walking indoors. The PCoA result showed that the samples were clustered by geographic origin. The shoeprint showed a more rapid turnover of microbial community than the shoe sole during indoor walking. The result of FEAST showed that the microbial communities of shoe sole and shoeprint were mainly (shoe sole, 86.21∼92.34 %; shoeprint, 61.66∼90.41 %) from the soil of the outdoor ground where the individual recently walked, and a small portion (shoe sole, 0.68∼3.33 %; shoeprint, 1.43∼27.14 %) from the indoor dust. Based on the matching of microbial communities between geolocation and shoe sole or shoeprint, we were able to infer the recent geolocation of the individual with relatively high accuracy using the random forest prediction model (shoe sole: 100.00 %, shoeprint: 93.33∼100.00 %). Overall, we are able to accurately infer the geolocation of an individual’s most recent outdoor walk based on the microbiota of shoe sole and shoeprint, even though these microbiotas show a turnover when walking indoor floor. The pilot study was expected to provide a potential method for tracing recent geolocation of suspects.     

Integrated multi-omics profiling yields a clinically relevant molecular classification for esophageal squamous cell carcinoma

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