ARIC: accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data.

Quantifying cell proportions, especially for rare cell types in some scenarios, is of great value in tracking signals associated with certain phenotypes or diseases. Although some methods have been proposed to infer cell proportions from multicomponent bulk data, they are substantially less effective for estimating the proportions of rare cell types which are highly sensitive to feature outliers and collinearity. Here we proposed a new deconvolution algorithm named ARIC to estimate cell type proportions from gene expression or DNA methylation data. ARIC employs a novel two-step marker selection strategy, including collinear feature elimination based on the component-wise condition number and adaptive removal of outlier markers. This strategy can systematically obtain effective markers for weighted $\upsilon$-support vector regression to ensure a robust and precise rare proportion prediction. We showed that ARIC can accurately estimate fractions in both DNA methylation and gene expression data from different experiments. We further applied ARIC to the survival prediction of ovarian cancer and the condition monitoring of chronic kidney disease, and the results demonstrate the high accuracy and robustness as well as clinical potentials of ARIC. Taken together, ARIC is a promising tool to solve the deconvolution problem of bulk data where rare components are of vital importance.

Evaluating methylation of human ribosomal DNA at each CpG site reveals its utility for cancer detection using cell-free DNA.

Ribosomal deoxyribonucleic acid (DNA) (rDNA) repeats are tandemly located on five acrocentric chromosomes with up to hundreds of copies in the human genome. DNA methylation, the most well-studied epigenetic mechanism, has been characterized for most genomic regions across various biological contexts. However, rDNA methylation patterns remain largely unexplored due to the repetitive structure. In this study, we designed a specific mapping strategy to investigate rDNA methylation patterns at each CpG site across various physiological and pathological processes. We found that CpG sites on rDNA could be categorized into two types. One is within or adjacent to transcribed regions; the other is distal to transcribed regions. The former shows highly variable methylation levels across samples, while the latter shows stable high methylation levels in normal tissues but severe hypomethylation in tumors. We further showed that rDNA methylation profiles in plasma cell-free DNA could be used as a biomarker for cancer detection. It shows good performances on public datasets, including colorectal cancer [area under the curve (AUC) = 0.85], lung cancer (AUC = 0.84), hepatocellular carcinoma (AUC = 0.91) and in-house generated hepatocellular carcinoma dataset (AUC = 0.96) even at low genome coverage (<1×). Taken together, these findings broaden our understanding of rDNA regulation and suggest the potential utility of rDNA methylation features as disease biomarkers.

DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads.

Detecting cancer signals in cell-free DNA (cfDNA) high-throughput sequencing data is emerging as a novel noninvasive cancer detection method. Due to the high cost of sequencing, it is crucial to make robust and precise predictions with low-depth cfDNA sequencing data. Here we propose a novel approach named DISMIR, which can provide ultrasensitive and robust cancer detection by integrating DNA sequence and methylation information in plasma cfDNA whole-genome bisulfite sequencing (WGBS) data. DISMIR introduces a new feature termed as 'switching region' to define cancer-specific differentially methylated regions, which can enrich the cancer-related signal at read-resolution. DISMIR applies a deep learning model to predict the source of every single read based on its DNA sequence and methylation state and then predicts the risk that the plasma donor is suffering from cancer. DISMIR exhibited high accuracy and robustness on hepatocellular carcinoma detection by plasma cfDNA WGBS data even at ultralow sequencing depths. Further analysis showed that DISMIR tends to be insensitive to alterations of single CpG sites' methylation states, which suggests DISMIR could resist to technical noise of WGBS. All these results showed DISMIR with the potential to be a precise and robust method for low-cost early cancer detection.

cfDNApipe: a comprehensive quality control and analysis pipeline for cell-free DNA high-throughput sequencing data.

MOTIVATION: Cell-free DNA (cfDNA) is gaining substantial attention from both biological and clinical fields as a promising marker for liquid biopsy. Many aspects of disease-related features have been discovered from cfDNA high-throughput sequencing (HTS) data. However, there is still a lack of integrative and systematic tools for cfDNA HTS data analysis and quality control (QC). RESULTS: Here, we propose cfDNApipe, an easy-to-use and systematic python package for cfDNA whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) data analysis. It covers the entire analysis pipeline for the cfDNA data, including raw sequencing data processing, QC and sophisticated statistical analysis such as detecting copy number variations (CNVs), differentially methylated regions and DNA fragment size alterations. cfDNApipe provides one-command-line-execution pipelines and flexible application programming interfaces for customized analysis. AVAILABILITY AND IMPLEMENTATION: https://xwanglabthu.github.io/cfDNApipe/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

YTHDF3 modulates hematopoietic stem cells by recognizing RNA m6A modification on Ccnd1.

Hematopoietic stem cells (HSC) give rise to the cells of the blood system over the whole lifespan. N6-methyladenosine (m6A), the most prevalent RNA modification, modulates gene expression via the processes of "writing" and "reading". Recent studies showed that m6A "writer" genes (Mettl3 and Mettl14) play an essential role in HSC. However, which reader deciphers the m6A modification to modulate HSC remains unknown. In this study, we observed that dysfunction of Ythdf3 and Ccnd1 severely impaired the reconstitution capacity of HSC, which phenocopies Mettl3-deficient HSC. Dysfunction of Ythdf3 and Mettl3 results in a translational defect of Ccnd1. Ythdf3 and Mettl3 regulate HSC by transmitting m6A RNA methylation on the 5' untranslated region of Ccnd1. Enforced Ccnd1 expression completely rescued the defect of Ythdf3-/- HSC and partially rescued Mettl3-compromised HSC. Taken together, this study identified, for the first time, that Ccnd1 is the target of METTL3 and YTHDF3 to transmit the m6A RNA methylation signal and thereby regulate the reconstitution capacity of HSC.

The role of HSP40 in cancer: Recent advances.

Heat shock proteins (HSPs) are a family of proteins involved in protein folding and maturation that are expressed by cells in response to stressors including heat shock. Recent studies have demonstrated that HSPs play major roles in carcinogenesis by regulating angiogenesis, cell proliferation, migration, invasion, metastasis, apoptosis, as well as therapy resistance to certain anticancer drugs. Despite being the largest and most diverse subgroup of the HSP family, HSP40 (DNAJ) is an understudied family of co-chaperones. HSP40 family members are also known to be involved in various types of cancers. In this article, we review the involvement of human HSP40 family members in various aspects of cancer biology. In addition, we highlight the possible potential of HSP40 as a tumor biomarker or drug target for improving the prognosis and treatment of cancer patients in the future.

[Practical stability of whole-genome bisulfite sequencing using plasma cell-free DNA].

With the development of liquid biopsy technology, plasma cell-free DNA (cfDNA) becomes one of the research hotspots. Whole-genome bisulfite sequencing of plasma cell-free DNA has shown great potential medical applications such as cancer detection. However, the practical stability evaluation is still lacking. In this study, plasma cell-free DNA samples from two volunteers at different time were collected and prepared for sequencing in multiple laboratories. The library preparation strategies include pre-bisulfite, post-bisulfite and regular whole-genome sequencing. We established a set of quality control references for plasma cell-free DNA sequencing data and evaluated practical stability of blood collection, DNA extraction, and library preparation and sequencing depth. This work provided a technical practice guide for the application of plasma cfDNA methylation sequencing for clinical applications.