Cell-type specific methylation changes in the newborn child associated to obstetric pain relief.

Although it is widely known that various pharmaceuticals affect the methylome, the knowledge of the effects from anesthesia is limited, and nearly nonexistent regarding the effects of obstetric anesthesia on the newborn child. Using sequencing based-methylation data and a reference-based statistical deconvolution approach we performed methylome-wide association studies (MWAS) of neonatal whole blood, and for each cell-type specifically, to detect methylation variations that are associated with the pain relief administered to the mother during delivery. Significant findings were replicated in a different dataset and followed-up with gene ontology analysis to pinpoint biological functions of potential relevance to these neonatal methylation alterations. The MWAS analyses detected methylome-wide significant (q<0.1) alterations in the newborn for laughing gas in granulocytes (two CpGs, p<5.50x10-9, q = 0.067), and for pudendal block in monocytes (five CpGs across three loci, p<1.51 x10-8, q = 0.073). Suggestively significant findings (p<1.00x10-6) were detected for both treatments for bulk and all cell-types, and replication analyses showed consistent significant enrichment (odds ratios ranging 3.47-39.02; p<4.00×10-4) for each treatment, suggesting our results are robust. In contrast, we did not observe any overlap across treatments, suggesting that the treatments are associated with different alterations of the neonatal blood methylome. Gene ontology analyses of the replicating suggestively significant results indicated functions related to, for example, cell differentiation, intracellular membrane-bound organelles and calcium transport. In conclusion, for the first time, we investigated and detected effect of obstetric pain-relief on the blood methylome in the newborn child. The observed differences suggest that anesthetic treatment, such as laughing gas or pudendal block, may alter the neonatal methylome in a cell-type specific manner. Some of the observed alterations are part of gene ontology terms that previously have been suggested in relation to anesthetic treatment, supporting its potential role also in obstetric anesthesia.

A targeted solution for estimating the cell-type composition of bulk samples.

BACKGROUND: To avoid false-positive findings and detect cell-type specific associations in methylation and transcription investigations with bulk samples, it is critical to know the proportions of the major cell-types. RESULTS: We present a novel approach that allows for precise estimation of cell-type proportions using only a few highly informative methylation markers. The most reliable estimates were obtained with 17 amplicons (34 CpGs) using the MuSiC estimator, for which the average correlations between the estimated and the true cell-type proportions were 0.889. Furthermore, the estimates were not significantly different from the true values (P = 0.95) indicating that the estimator is unbiased and the standard deviation of the estimates further indicate high precision. Moreover, the overall variability of the estimates as measured by the Root Mean Squared Error (RMSE), which is a function of both bias and precision, was low (mean RMSE = 0.038). Taken together, these results indicate that the approach produced reliable estimates that are both unbiased and highly precise. CONCLUSION: This cost-effective approach for estimating cell-type proportions in bulk samples allows for enhanced targeted analysis, which in turn will minimize the risk of reporting false-positive findings and allowing for detection of cell-type specific associations. The approach is applicable across platforms and can be extended to assess cell-type proportions for various tissues.