Abstract
We report a new class of artifacts in DNA methylation measurements from Illumina HumanMethylation450 and MethylationEPIC arrays. These artifacts reflect failed hybridization to target DNA, ...often due to germline or somatic deletions and manifest as incorrectly reported intermediate methylation. The artifacts often survive existing preprocessing pipelines, masquerade as epigenetic alterations and can confound discoveries in epigenome-wide association studies and studies of methylation-quantitative trait loci. We implement a solution, P-value with out-of-band (OOB) array hybridization (pOOBAH), in the R package SeSAMe. Our method effectively masks deleted and hyperpolymorphic regions, reducing or eliminating spurious reports of epigenetic silencing at oft-deleted tumor suppressor genes such as CDKN2A and RB1 in cases with somatic deletions. Furthermore, our method substantially decreases technical variation whilst retaining biological variation, both within and across HM450 and EPIC platform measurements. SeSAMe provides a light-weight, modular DNA methylation data analysis suite, with a performant implementation suitable for efficient analysis of thousands of samples.
The minfi package is widely used for analyzing Illumina DNA methylation array data. Here we describe modifications to the minfi package required to support the HumanMethylationEPIC ('EPIC') array ...from Illumina. We discuss methods for the joint analysis and normalization of data from the HumanMethylation450 ('450k') and EPIC platforms. We introduce the single-sample Noob ( ssNoob ) method, a normalization procedure suitable for incremental preprocessing of individual methylation arrays and conclude that this method should be used when integrating data from multiple generations of Infinium methylation arrays. We show how to use reference 450k datasets to estimate cell type composition of samples on EPIC arrays. The cumulative effect of these updates is to ensure that minfi provides the tools to best integrate existing and forthcoming Illumina methylation array data.
The minfi package version 1.19.12 or higher is available for all platforms from the Bioconductor project.
khansen@jhsph.edu.
Supplementary data are available at Bioinformatics online.
We propose a novel approach to background correction for Infinium HumanMethylation data to account for technical variation in background fluorescence signal. Our approach capitalizes on a new use for ...the Infinium I design bead types to measure non-specific fluorescence in the colour channel opposite of their design (Cy3/Cy5). This provides tens of thousands of features for measuring background instead of the much smaller number of negative control probes on the platforms (n = 32 for HumanMethylation27 and n = 614 for HumanMethylation450, respectively). We compare the performance of our methods with existing approaches, using technical replicates of both mixture samples and biological samples, and demonstrate that within- and between-platform artefacts can be substantially reduced, with concomitant improvement in sensitivity, by the proposed methods.
Abstract
Data from both bulk and single-cell whole-genome DNA methylation experiments are under-utilized in many ways. This is attributable to inefficient mapping of methylation sequencing reads, ...routinely discarded genetic information, and neglected read-level epigenetic and genetic linkage information. We introduce the BISulfite-seq Command line User Interface Toolkit (BISCUIT) and its companion R/Bioconductor package, biscuiteer, for simultaneous extraction of genetic and epigenetic information from bulk and single-cell DNA methylation sequencing. BISCUIT’s performance, flexibility and standards-compliant output allow large, complex experimental designs to be characterized on clinical timescales. BISCUIT is particularly suited for processing data from single-cell DNA methylation assays, with its excellent scalability, efficiency, and ability to greatly enhance mappability, a key challenge for single-cell studies. We also introduce the epiBED format for single-molecule analysis of coupled epigenetic and genetic information, facilitating the study of cellular and tissue heterogeneity from DNA methylation sequencing.
Graphical Abstract
Graphical Abstract
The recent COVID-19 pandemic is a treatment challenge in the acute infection stage but the recognition of chronic COVID-19 symptoms termed post-acute sequelae SARS-CoV-2 infection (PASC) may affect ...up to 30% of all infected individuals. The underlying mechanism and source of this distinct immunologic condition three months or more after initial infection remains elusive. Here, we investigated the presence of SARS-CoV-2 S1 protein in 46 individuals. We analyzed T-cell, B-cell, and monocytic subsets in both severe COVID-19 patients and in patients with post-acute sequelae of COVID-19 (PASC). The levels of both intermediate (CD14+, CD16+) and non-classical monocyte (CD14Lo, CD16+) were significantly elevated in PASC patients up to 15 months post-acute infection compared to healthy controls (P=0.002 and P=0.01, respectively). A statistically significant number of non-classical monocytes contained SARS-CoV-2 S1 protein in both severe (P=0.004) and PASC patients (P=0.02) out to 15 months post-infection. Non-classical monocytes were sorted from PASC patients using flow cytometric sorting and the SARS-CoV-2 S1 protein was confirmed by mass spectrometry. Cells from 4 out of 11 severe COVID-19 patients and 1 out of 26 PASC patients contained ddPCR+ peripheral blood mononuclear cells, however, only fragmented SARS-CoV-2 RNA was found in PASC patients. No full length sequences were identified, and no sequences that could account for the observed S1 protein were identified in any patient. That non-classical monocytes may be a source of inflammation in PASC warrants further study.
Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature ...detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.
A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases--men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.
Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67-0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.
A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.
Abstract Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, ...but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and of tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.
The Hedgehog (Hh) signaling pathway is activated in many cancers and is a promising target for therapeutic development. Deletions in the receptor Patched (PTCH) or activating mutations in Smoothened ...(SMO) have been reported in basal cell carcinoma and medulloblastoma, but are largely absent in most tumor types. Therefore, the mechanism of pathway activation in most cancers, including hematological malignancies, remains unknown. In normal tissues, Hh pathway activation via PTCH/SMO causes an increase in the downstream transcriptional activator GLI1 and a decrease in the GLI3 transcriptional repressor (GLI3R). In this article, we confirm that the Hh pathway is active in acute myeloid leukemia (AML), however, this activity is largely independent of SMO. Epigenetic and gene expression analysis of The Cancer Genome Atlas AML data set reveals that GLI3 expression is silenced in most AML patient samples, and the GLI3 locus is abnormally methylated. We show that GLI3R is required for the therapeutic effect of SMO antagonists in AML samples and restoration of GLI3R suppresses the growth of AML. We additionally demonstrate that GLI3R represses AML growth by downregulating AKT expression. In summary, this study provides the first evidence that GLI3R plays an essential role in SMO-independent Hh signaling in AML, and suggests that GLI3R could serve as a potential biomarker for patient selection in SMO antagonist clinical trials. Furthermore, these data support rational combinations of hypomethylating agents with SMO antagonists in clinical trials.
•GLI3R inhibits Hh signaling and is required for response to SMO antagonist in AML.•GLI3 is silenced in AML, and decitabine restores GLI3 expression and leads to modulation of Hh signaling.
Prostate cancers remain indolent in the majority of individuals but behave aggressively in a minority. The molecular basis for this clinical heterogeneity remains incompletely understood. Here we ...characterize a long noncoding RNA termed SChLAP1 (second chromosome locus associated with prostate-1; also called LINC00913) that is overexpressed in a subset of prostate cancers. SChLAP1 levels independently predict poor outcomes, including metastasis and prostate cancer-specific mortality. In vitro and in vivo gain-of-function and loss-of-function experiments indicate that SChLAP1 is critical for cancer cell invasiveness and metastasis. Mechanistically, SChLAP1 antagonizes the genome-wide localization and regulatory functions of the SWI/SNF chromatin-modifying complex. These results suggest that SChLAP1 contributes to the development of lethal cancer at least in part by antagonizing the tumor-suppressive functions of the SWI/SNF complex.
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