Genetic risk of osteoarthritis operates during human skeletogenesis.

Osteoarthritis (OA) is a polygenic disease of older people resulting in the breakdown of cartilage within articular joints. Although it is a leading cause of disability, there are no disease-modifying therapies. Evidence is emerging to support the origins of OA in skeletogenesis. Whereas methylation quantitative trait loci (mQTLs) co-localizing with OA genome-wide association study signals have been identified in aged human cartilage and used to identify effector genes and variants, such analyses have never been conducted during human development. Here, for the first time, we have investigated the developmental origins of OA genetic risk at seven well-characterized OA risk loci, comprising 39 OA-mQTL CpGs, in human fetal limb (FL) and cartilage (FC) tissues using a range of molecular genetic techniques. We identified significant OA-mQTLs at 14 and 29 CpGs in FL and FC tissues, respectively, and compared our results with aged cartilage samples (AC). Differential methylation was observed at 26 sites between FC and AC, with the majority becoming actively hypermethylated in old age. Notably, 6/9 OA effector genes showed allelic expression imbalances during fetal development. Finally, we conducted ATAC-sequencing in cartilage from the developing and aged hip and knee to identify accessible chromatin regions and found enrichment for transcription factor binding motifs including SOX9 and FOS/JUN. For the first time, we have demonstrated the activity of OA-mQTLs and expression imbalance of OA effector genes during human skeletogenesis. We show striking differences in the spatiotemporal function of these loci, contributing to our understanding of OA aetiology, with implications for the timing and strategy of pharmacological interventions.

Multi-Tissue Epigenetic and Gene Expression Analysis Combined With Epigenome Modulation Identifies RWDD2B as a Target of Osteoarthritis Susceptibility.

OBJECTIVE: Osteoarthritis (OA) is polygenic, with more than 90 risk loci currently mapped, including at the single-nucleotide polymorphism rs6516886. Previous analysis of OA cartilage DNA identified 6 CpG dinucleotides whose methylation levels correlated with the rs6516886 genotype, forming methylation quantitative trait loci (mQTLs). We undertook this study to investigate these mQTLs and to map expression quantitative trait loci (eQTLs) across joint tissues in order to prioritize a particular gene as a target of the rs6516886 association effect. METHODS: Nucleic acids were extracted from the cartilage, fat pad, synovium, and peripheral blood from OA patients. Methylation of CpGs and allelic expression imbalance of potential target genes were assessed by pyrosequencing. A chondrocyte cell line expressing deactivated Cas9 (dCas9)-TET1 was used to directly alter CpG methylation levels, with effects on gene expression quantified by polymerase chain reaction. RESULTS: Multiple mQTLs were detected, with effects strongest in joint tissues and with methylation at CpG cg20220242 correlating most significantly with the rs6516886 genotype. CpG cg20220242 is located upstream of RWDD2B. Significant rs6516886 eQTLs were observed for this gene, with the OA risk-conferring allele of rs6516886 correlating with reduced expression CpG methylation also correlated with allelic expression of RWDD2B, forming methylation-expression QTLs (meQTLs). Deactivated Cas9-TET1 reduction in the methylation of cg20220242 increased expression of RWDD2B. CONCLUSION: The rs6516886 association signal is a multi-tissue meQTL involving cg20220242 and acting on RWDD2B. Modulating CpG methylation reverses the impact of the risk allele. RWDD2B codes for a protein about which little is currently known. Its further analysis as a target of OA genetic risk will provide novel insight into this complex disease.

Estimating crime scene temperatures from nearby meteorological station data.

The importance of temperature data in minimum postmortem interval (minPMI) estimations in criminal investigations is well known. To maximise the accuracy of minPMI estimations, it is imperative to investigate the different components involved in temperature modelling, such as the duration of temperature data logger placement at the crime scene and choice of nearest weather station to compare the crime scene data to. Currently, there is no standardised practice on how long to leave the temperature data logger at the crime scene and the effects of varying logger duration are little known. The choice of the nearest weather station is usually made based on availability and accessibility of data from weather stations in the crime scene vicinity. However, there are no guidelines on what to look for to maximise the comparability of weather station and crime scene temperatures. Linear regression analysis of scene data with data from weather stations with varying time intervals, distances, altitudes and microclimates showed the greatest goodness of fit (R2), i.e. the highest compatibility between datasets, after 4-10 days. However, there was no significant improvement in estimation of crime scene temperatures beyond a 5-day regression period. The smaller the distance between scene and weather station and the higher the similarity in environment, such as altitude and geographical area, resulted in greater compatibility between datasets. Overall, the study demonstrated the complexity of choosing the most comparable weather station to the crime scene, especially because of a high variation in seasonal temperature and numerous influencing factors such as geographical location, urban 'heat island effect' and microclimates. Despite subtle differences, for both urban and rural areas an optimal data fit was generally reached after about five consecutive days within a radius of up to 30 km of the 'crime scene'. With increasing distance and differing altitudes, a lower overall data fit was observed, and a diminishing increase in R2 values was reached after 4-10 consecutive days. These results demonstrate the need for caution regarding distances and climate differences when using weather station data for retrospective regression analyses for estimating temperatures at crime scenes. However, the estimates of scene temperatures from regression analysis were better than simply using the temperatures from the nearest weather station. This study provides recommendations for data logging duration of operation, and a baseline for further research into producing standard guidelines for increasing the accuracy of minPMI estimations and, ultimately, greater robustness of forensic entomology evidence in court.

Optimising crime scene temperature collection for forensic entomology casework.

The value of minimum post-mortem interval (minPMI) estimations in suspicious death investigations from insect evidence using temperature modelling is indisputable. In order to investigate the reliability of the collected temperature data used for modelling minPMI, it is necessary to study the effects of data logger location on the accuracy and precision of measurements. Digital data logging devices are the most commonly used temperature measuring devices in forensic entomology, however, the relationship between ambient temperatures (measured by loggers) and body temperatures has been little studied. The placement of loggers in this study in three locations (two outdoors, one indoors) had measurable effects when compared with actual body temperature measurements (simulated with pig heads), some more significant than others depending on season, exposure to the environment and logger location. Overall, the study demonstrated the complexity of the question of optimal logger placement at a crime scene and the potential impact of inaccurate temperature data on minPMI estimations, showing the importance of further research in this area and development of a standard protocol. Initial recommendations are provided for data logger placement (within a Stevenson Screen where practical), situations to avoid (e.g. placement of logger in front of windows when measuring indoor temperatures), and a baseline for further research into producing standard guidelines for logger placement, to increase the accuracy of minPMI estimations and, thereby, the reliability of forensic entomology evidence in court.