How individual genes are regulated from a mitochondrial polycistronic transcript to have variable expression remains an enigma. Here, through bisulfite sequencing and strand-specific mapping, we show mitochondrial genomes in humans and other animals are strongly biased to light (L)-strand non-CpG methylation with conserved peak loci preferentially located at gene-gene boundaries, which was also independently validated by MeDIP and FspEI digestion. Such mtDNA methylation patterns are conserved across different species and developmental stages but display dynamic local or global changes during development and aging. Knockout of DNMT3A alone perturbed mtDNA regional methylation patterns, but not global levels, and altered mitochondrial gene expression, copy number, and oxygen respiration. Overexpression of DNMT3A strongly increased mtDNA methylation and strand bias. Overall, methylation at gene bodies and boundaries was negatively associated with mitochondrial transcript abundance and also polycistronic transcript processing. Furthermore, HPLC-MS confirmed the methylation signals on mitochondria DNA. Together, these data provide highresolution mtDNA methylation maps that revealed a strand-specific non-CpG methylation, its dynamic regulation, and its impact on the polycistronic mitochondrial transcript processing.

Single-cell RNA sequencing (scRNA-seq) is a powerful method for dissecting intercellular heterogeneity during development. Conventional trajectory analysis provides only a pseudotime of development, and often discards cell-cycle events as confounding factors. Here using matched cell population RNA-seq (cpRNA-seq) as a reference, we developed an “iCpSc” package for integrative analysis of cpRNA-seq and scRNA-seq data. By generating a computational model for reference “biological differentiation time” using cell population data and applying it to single-cell data, we unbiasedly associated cell-cycle checkpoints to the internal molecular timer of single cells. Through inferring a network flow from cpRNA-seq to scRNA-seq data, we predicted a role of M phase in controlling the speed of neural differentiation of mouse embryonic stem cells, and validated it through gene knockout (KO) experiments. By linking temporally matched cpRNA-seq and scRNA-seq data, our approach provides an effective and unbiased approach for identifying developmental trajectory and timing-related regulatory events.

Objective Recently, the incidence of thyroid cancer as well as obesity has dramatically increased worldwide. Whether obesity contributes to the development of differentiated thyroid cancer (DTC) remains controversial. We evaluated the relationship between anthropometric measurements and DTC risk. Design/Patients/Measurements A large frequency‐matched case‐control study based on hospital data was performed. A total of 10 668 DTC patients and 11 858 controls were enrolled. Body mass index (BMI), body surface area (BSA) and body fat percentage (BF%) were calculated. An unconditional logistic regression model was applied. Results The univariate analysis showed a significant increase in DTC risk with increased height, weight, BMI, BSA and BF%. The multivariate analysis also showed a positive relationship. Based on the Chinese BMI (CN‐BMI) classification, for women of all ages, the ORs for DTC risk in overweight and obesity were 1.151 (1.037‐1277) and 1.292 (1.092‐1.528), respectively. For men under 50, the ORs were 1.221 (1.014‐1.469) and 1.520 (1.202‐1.923), respectively, but the ORs for men over 50 were not significant. Additionally, BSA showed a significant association with DTC risk for both sexes under 50 (P = .02 and P < .001). BF% remained significant only for women under 50 (P = .003). However, for both sexes over 50, neither BSA nor BF% was significantly associated with DTC risk. Based on The World Health Organization BMI (WHO‐BMI) classification, for all women and men over 50, the results were consistent with CN‐BMI. For men under 50, BF%, but not BMI, showed a significant association with DTC risk. Conclusion BMI, BSA and BF% were positively correlated with the risk of DTC, which was potentially affected by age and sex.