The 5′-cap structure is a distinct feature of eukaryotic mRNAs, and eukaryotic viruses generally modify the 5′-end of viral RNAs to mimic cellular mRNA structure, which is important for RNA stability, protein translation and viral immune escape. SARS coronavirus (SARS-CoV) encodes two S-adenosyl-L-methionine (SAM)-dependent methyltransferases (MTase) which sequentially methylate the RNA cap at guanosine-N7 and ribose 2′-O positions, catalyzed by nsp14 N7-MTase and nsp16 2′-O-MTase, respectively. A unique feature for SARS-CoV is that nsp16 requires non-structural protein nsp10 as a stimulatory factor to execute its MTase activity. Here we report the biochemical characterization of SARS-CoV 2′-O-MTase and the crystal structure of nsp16/nsp10 complex bound with methyl donor SAM. We found that SARS-CoV nsp16 MTase methylated m7GpppA-RNA but not m7GpppG-RNA, which is in contrast with nsp14 MTase that functions in a sequence-independent manner. We demonstrated that nsp10 is required for nsp16 to bind both m7GpppA-RNA substrate and SAM cofactor. Structural analysis revealed that nsp16 possesses the canonical scaffold of MTase and associates with nsp10 at 1∶1 ratio. The structure of the nsp16/nsp10 interaction interface shows that nsp10 may stabilize the SAM-binding pocket and extend the substrate RNA-binding groove of nsp16, consistent with the findings in biochemical assays. These results suggest that nsp16/nsp10 interface may represent a better drug target than the viral MTase active site for developing highly specific anti-coronavirus drugs. The distinctive feature of eukaryotic mRNAs is the presence of methylated cap structure that is required for mRNA stability and protein translation. As all viruses employ cellular ribosomes for protein translation, most cytoplasmically replicating eukaryotic viruses including coronaviruses have evolved strategies to cap their RNAs. It was shown very recently that ribose 2′-O-methylation in the cap structure of viral RNAs plays an important role in viral escape from innate immune recognition. The 2′-O-methyltransferase (2′-O-MTase) encoded by SARS coronavirus is composed of two subunits, the catalytic subunit nsp16 and the stimulatory subunit nsp10, which is different from all other known 2′-O-MTases that are partner-independent. Here we show that the role of nsp10 is to promote nsp16 to bind capped RNA substrate and the methyl donor S-adenosyl-L-methionine (SAM). We solved the crystal structure of the nsp16/nsp10/SAM complex, and the structural analysis revealed that the details of the inter-molecular interactions and indicated that nsp10 may stabilize the SAM-binding pocket and extend the capped RNA-binding groove. The interaction interface of nsp16/nsp10 is unique for coronaviruses and thus may provide an attractive target for developing specific antiviral drugs for control of coronaviruses including the deadly SARS coronavirus.

Objective: The lack of highly sensitive and specific diagnostic biomarkers is a major contributor to the poor outcomes of patients with hepatocellular carcinoma (HCC). We sought to develop a non-invasive diagnostic approach using circulating cell-free DNA (cfDNA) for the early detection of HCC. Design: Applying the 5hmC-Seal technique, we obtained genome-wide 5-hydroxymethylcytosines (5hmC) in cfDNA samples from 2554 Chinese subjects: 1204 patients with HCC, 392 patients with chronic hepatitis B virus infection (CHB) or liver cirrhosis (LC) and 958 healthy individuals and patients with benign liver lesions. A diagnostic model for early HCC was developed through case-control analyses using the elastic net regularisation for feature selection. Results: The 5hmC-Seal data from patients with HCC showed a genome-wide distribution enriched with liver-derived enhancer marks. We developed a 32-gene diagnostic model that accurately distinguished early HCC (stage 0/A) based on the Barcelona Clinic Liver Cancer staging system from non-HCC (validation set: area under curve (AUC)=88.4%; (95% CI 85.8% to 91.1%)), showing superior performance over α-fetoprotein (AFP). Besides detecting patients with early stage or small tumours (eg, ≤2.0 cm) from non-HCC, the 5hmC model showed high capacity for distinguishing early HCC from high risk subjects with CHB or LC history (validation set: AUC=84.6%; (95% CI 80.6% to 88.7%)), also significantly outperforming AFP. Furthermore, the 5hmC diagnostic model appeared to be independent from potential confounders (eg, smoking/alcohol intake history). Conclusion: We have developed and validated a non-invasive approach with clinical application potential for the early detection of HCC that are still surgically resectable in high risk individuals.

Motivation: High-dimensional DNA methylation markers may mediate pathways linking environmental exposures with health outcomes. However, there is a lack of analytical methods to identify significant mediators for high-dimensional mediation analysis.

Biological measures of aging are important for understanding the health of an aging population, with epigenetics particularly promising. Previous studies found that tumor tissue is epigenetically older than its donors are chronologically. We examined whether blood Δage (the discrepancy between epigenetic and chronological ages) can predict cancer incidence or mortality, thus assessing its potential as a cancer biomarker. In a prospective cohort, Δage and its rate of change over time were calculated in 834 blood leukocyte samples collected from 442 participants free of cancer at blood draw. About 3-5 years before cancer onset or death, Δage was associated with cancer risks in a dose-responsive manner (P = 0.02) and a one-year increase in Δage was associated with cancer incidence (HR: 1.06, 95% CI: 1.02-1.10) and mortality (HR: 1.17, 95% CI: 1.07-1.28). Participants with smaller Δage and decelerated epigenetic aging over time had the lowest risks of cancer incidence (P = 0.003) and mortality (P = 0.02). Δage was associated with cancer incidence in a 'J-shaped' manner for subjects examined pre-2003, and with cancer mortality in a time-varying manner. We conclude that blood epigenetic age may mirror epigenetic abnormalities related to cancer development, potentially serving as a minimally invasive biomarker for cancer early detection.

DNA 5-hydroxymethylcytosine (5hmC) modification is known to be associated with gene transcription and frequently used as a mark to investigate dynamic DNA methylation conversion during mammalian development and in human diseases. However, the lack of genome-wide 5hmC profiles in different human tissue types impedes drawing generalized conclusions about how 5hmC is implicated in transcription activity and tissue specificity. To meet this need, we describe the development of a 5hmC tissue map by characterizing the genomic distributions of 5hmC in 19 human tissues derived from ten organ systems. Subsequent sequencing results enabled the identification of genome-wide 5hmC distributions that uniquely separates samples by tissue type. Further comparison of the 5hmC profiles with transcriptomes and histone modifications revealed that 5hmC is preferentially enriched on tissue-specific gene bodies and enhancers. Taken together, the results provide an extensive 5hmC map across diverse human tissue types that suggests a potential role of 5hmC in tissue-specific development; as well as a resource to facilitate future studies of DNA demethylation in pathogenesis and the development of 5hmC as biomarkers.

DNA methylation in repetitive elements (RE) suppresses their mobility and maintains genomic stability, and decreases in it are frequently observed in tumor and/or surrogate tissues. Averaging methylation across RE in genome is widely used to quantify global methylation. However, methylation may vary in specific RE and play diverse roles in disease development, thus averaging methylation across RE may lose significant biological information. The ambiguous mapping of short reads by and high cost of current bisulfite sequencing platforms make them impractical for quantifying locus-specific RE methylation. Although microarray-based approaches (particularly Illumina's Infinium methylation arrays) provide cost-effective and robust genome-wide methylation quantification, the number of interrogated CpGs in RE remains limited. We report a random forest-based algorithm (and corresponding R package, REMP) that can accurately predict genome-wide locus-specific RE methylation based on Infinium array profiling data. We validated its prediction performance using alternative sequencing and microarray data. Testing its clinical utility with The Cancer Genome Atlas data demonstrated that our algorithm offers more comprehensively extended locus-specific RE methylation information that can be readily applied to large human studies in a cost-effective manner. Our work has the potential to improve our understanding of the role of global methylation in human diseases, especially cancer.