The UCSC Genome Browser database: 2019 update

APOBEC3A regulates transcription from interferon-stimulated response elements

APOBEC3A (A3A) is a cytidine deaminase that inactivates a variety of viruses through introduction of lethal mutations to the viral genome. Additionally, A3A can suppress HIV-1 transcription in a deaminase-independent manner by binding to the long terminal repeat of proviral HIV-1. However, it is unknown whether A3A targets additional host genomic loci for repression. In this study, we found that A3A suppresses gene expression by binding TTTC doublets that are in close proximity to each other. However, one TTTC motif is sufficient for A3A binding. Because TTTC doublets are present in interferon (IFN)-stimulated response elements (ISRE), we hypothesized that A3A may impact IFN-stimulated gene (ISG) expression. After scanning the human genome for TTTC doublet occurrences, we discovered that these motifs are enriched in the proximal promoters of genes associated with antiviral responses and type I IFN (IFN-I) signaling. As a proof of principle, we examined whether A3A can impact ISG15 expression. We found that A3A binding to the ISRE inhibits phosphorylated STAT-1 binding and suppresses ISG15 induction in response to IFN-I treatment. Consistent with these data, our RNA-sequencing analyses indicate that A3A loss results in increased IFN-I–dependent induction of several ISGs. This study revealed that A3A plays an unexpected role in ISG regulation and suggests that A3A contributes to a negative feedback loop during IFN signaling.

Reconstructing SNP allele and genotype frequencies from GWAS summary statistics

The emergence of genome-wide association studies (GWAS) has led to the creation of large repositories of human genetic variation, creating enormous opportunities for genetic research and worldwide collaboration. Methods that are based on GWAS summary statistics seek to leverage such records, overcoming barriers that often exist in individual-level data access while also offering significant computational savings. Such summary-statistics-based applications include GWAS meta-analysis, with and without sample overlap, and case-case GWAS. We compare performance of leading methods for summary-statistics-based genomic analysis and also introduce a novel framework that can unify usual summary-statistics-based implementations via the reconstruction of allelic and genotypic frequencies and counts (ReACt). First, we evaluate ASSET, METAL, and ReACt using both synthetic and real data for GWAS meta-analysis (with and without sample overlap) and find that, while all three methods are comparable in terms of power and error control, ReACt and METAL are faster than ASSET by a factor of at least hundred. We then proceed to evaluate performance of ReACt vs an existing method for case-case GWAS and show comparable performance, with ReACt requiring minimal underlying assumptions and being more user-friendly. Finally, ReACt allows us to evaluate, for the first time, an implementation for calculating polygenic risk score (PRS) for groups of cases and controls based on summary statistics. Our work demonstrates the power of GWAS summary-statistics-based methodologies and the proposed novel method provides a unifying framework and allows further extension of possibilities for researchers seeking to understand the genetics of complex disease.

Assessing and assuring interoperability of a genomics file format

Motivation

Bioinformatics software tools operate largely through the use of specialized genomics file formats. Often these formats lack formal specification, making it difficult or impossible for the creators of these tools to robustly test them for correct handling of input and output. This causes problems in interoperability between different tools that, at best, wastes time and frustrates users. At worst, interoperability issues could lead to undetected errors in scientific results.

Results

We developed a new verification system, Acidbio, which tests for correct behavior in bioinformatics software packages. We crafted tests to unify correct behavior when tools encounter various edge cases—potentially unexpected inputs that exemplify the limits of the format. To analyze the performance of existing software, we tested the input validation of 80 Bioconda packages that parsed the Browser Extensible Data (BED) format. We also used a fuzzing approach to automatically perform additional testing. Of 80 software packages examined, 75 achieved less than 70% correctness on our test suite. We categorized multiple root causes for the poor performance of different types of software. Fuzzing detected other errors that the manually designed test suite could not. We also created a badge system that developers can use to indicate more precisely which BED variants their software accepts and to advertise the software’s performance on the test suite.

Availability and implementation

Acidbio is available at https://github.com/hoffmangroup/acidbio.

Supplementary information

Supplementary data are available at Bioinformatics online.

Incomplete lineage sorting and phenotypic evolution in marsupials

Incomplete lineage sorting (ILS) makes ancestral genetic polymorphisms persist during rapid speciation events, inducing incongruences between gene trees and species trees. ILS has complicated phylogenetic inference in many lineages, including hominids. However, we lack empirical evidence that ILS leads to incongruent phenotypic variation. Here, we performed phylogenomic analyses to show that the South American monito del monte is the sister lineage of all Australian marsupials, although over 31% of its genome is closer to the Diprotodontia than to other Australian groups due to ILS during ancient radiation. Pervasive conflicting phylogenetic signals across the whole genome are consistent with some of the morphological variation among extant marsupials. We detected hundreds of genes that experienced stochastic fixation during ILS, encoding the same amino acids in non-sister species. Using functional experiments, we confirm how ILS may have directly contributed to hemiplasy in morphological traits that were established during rapid marsupial speciation ca. 60 mya.

Targeting Circulating lncRNA ENST00000538705.1 Relieves Acute Coronary Syndrome via Modulating ALOX15

Objective

Acute coronary syndrome (ACS) is the most dangerous and deadly form of coronary heart disease. Herein, we aimed to explore ACS-specific circulating lncRNAs and their regulatory mechanisms.

Methods

This study collected serum samples from ACS patients and healthy controls for microarray analysis. Dysregulated circulating lncRNAs and mRNAs were determined with |log2fold − change| > 1 and p < 0.05. lncRNA-mRNA coexpression analysis was carried out. ENST00000538705.1 and ALOX15 expression was further verified in serum specimens. In human coronary artery endothelial cells (HCAECs), ENST00000538705.1 and ALOX15 were knocked out through transfecting specific siRNAs. Thereafter, proliferation and migration were investigated with CCK-8 and wound-healing assays. Myocardial infarction rat models were established and administrated with siRNAs against ENST00000538705.1 or ALOX15. Myocardial damage was investigated with H&E staining, and serum TC, LDL, and HDL levels were measured.

Results

Microarray analysis identified 353 dysregulated circulating lncRNAs and 441 dysregulated circulating mRNAs in ACS. Coexpression analysis indicated the interaction between ENST00000538705.1 and ALOX15. RT-qPCR confirmed the remarkable upregulation of circulating ENST00000538705.1 and ALOX15 in ACS patients. In HCAECs, ENST00000538705.1 knockdown lowered the expression of ALOX15 but ALOX15 did not alter the expression of ENST00000538705.1. Silencing ENST00000538705.1 or ALOX15 weakened the proliferation and migration of HCAECs. Additionally, knockdown of ENST00000538705.1 or ALOX15 relieved myocardial damage, decreased serum TC and LDL levels, and elevated HDL levels in myocardial infarction rats.

Conclusion

Collectively, our findings demonstrate that circulating ENST00000538705.1 facilitates ACS progression through modulating ALOX15, which provide potential targets for ACS treatment.

Sperm Autoantigenic Protein 17 Predicts the Prognosis and the Immunotherapy Response of Cancers: A Pan-Cancer Analysis

BACKGROUND

Sperm autoantigen protein 17 (SPA17) is a highly conserved mammalian protein that participates in the acrosome reaction during fertilization and is a recently reported member of the cancer-testicular antigen (CTA) family. It has been reported that the SPA17 expression is limited in adult somatic tissues and re-expressed in tumor tissues. Recently, studies have found that SPA17 regulates the progression of various cancers, but its role in cancer immunotherapy is not clear.

METHODS

The pan-cancer and normal tissue transcriptional data were acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets. We explored the SPA17 pan-cancer genomic alteration analysis in the cBioPortal webtool. The Human Protein Atlas (HPA) and ComPPI websites were used to mine the SPA17 protein information. We performed a western blotting assay to validate the upregulated SPA17 expression in clinical glioblastoma (GBM) samples. The univariate Cox regression and Kaplan-Meier method were used to assess the prognostic role of SPA17 in pan-cancer. Gene Set Enrichment Analysis (GSEA) was used to search the associated cancer hallmarks with SPA17 expression in each cancer type. TIMER2.0 was the main platform to investigate the immune cell infiltrations related to SPA17 in pan-cancer. The associations between SPA17 and immunotherapy biomarkers were performed by Spearman correlation analysis. The drug sensitivity information from the Connectivity Map (CMap) dataset was downloaded to perform SAP17-specific inhibitor sensitivity analysis.

FINDINGS

SPA17 was aberrantly expressed in most cancer types and exhibited prognosis predictive ability in various cancers. In addition, our results also show that SPA17 was significantly correlated with immune-activated hallmarks (including pathways and biological processes), immune cell infiltrations, and immunoregulator expressions. The most exciting finding was that SPA17 could significantly predict anti-PDL1 and anti-PD1 therapy responses in cancer patients. Finally, specific inhibitors, like irinotecan and puromycin, which correlate with SPA17 expression in different cancer types, were also screened using Connectivity Map (CMap).

CONCLUSIONS

Our results reveal that SPA17 was abnormally expressed in cancer tissues, and this expression pattern could be associated with immune cell infiltrations in tumor microenvironments. Clinically, SPA17 not only acted as a potent prognostic factor to predict the clinical outcomes of cancer patients but was also a promising immunotherapy predictive biomarker for cancer patients treated with immune-checkpoint inhibitors (ICIs).

SSRTool: a web tool for evaluating RNA secondary structure predictions based on species-specific functional interpretability

RNA secondary structures can carry out essential cellular functions alone or interact with one another to form the hierarchical tertiary structures. Experimental structure identification approa ches can show the in vitro structures of RNA molecules. However, they usually have limits in the resolution and are costly. In silico structure prediction tools are thus primarily relied on for pre-experiment analysis. Various structure prediction models have been developed over the decades. Since these tools are usually used before knowing the actual RNA structures, evaluating and ranking the pile of secondary structure predictions of a given sequence is essential in computational analysis. In this research, we implemented a web service called SSRTool (RNA Secondary Structure prediction Ranking Tool) to assist in the ranking and evaluation of the generated predicted structures of a given sequence. Based on the computed species-specific interpretability significance in four common RNA structure–function aspects, SSRTool provides three functions along with visualization interfaces: (1) Rank user-generated predictions. (2) Provide an automated streamline of structure prediction and ranking for a given sequence. (3) Infer the functional aspects of a given structure. We demonstrated the applicability of SSRTool via real case studies and reported the similar trends between computed species-specific rankings and the corresponding prediction F1 values. The SSRTool web service is available online at https://cobisHSS0.im.nuk.edu.tw/SSRTool/, http://cosbi3.ee.ncku.edu.tw/SSRTool/, or the redirecting site https://github.com/cobisLab/SSRTool/.

Chronic psychological stress alters gene expression in rat colon epithelial cells promoting chromatin remodeling, barrier dysfunction and inflammation

Chronic stress is commonly associated with enhanced abdominal pain (visceral hypersensitivity), but the cellular mechanisms underlying how chronic stress induces visceral hypersensitivity are poorly understood. In this study, we examined changes in gene expression in colon epithelial cells from a rat model using RNA-sequencing to examine stress-induced changes to the transcriptome. Following chronic stress, the most significantly up-regulated genes included Atg16l1, Coq10b, Dcaf13, Nat2, Ptbp2, Rras2, Spink4 and down-regulated genes including Abat, Cited2, Cnnm2, Dab2ip, Plekhm1, Scd2, and Tab2. The primary altered biological processes revealed by network enrichment analysis were inflammation/immune response, tissue morphogenesis and development, and nucleosome/chromatin assembly. The most significantly down-regulated process was the digestive system development/function, whereas the most significantly up-regulated processes were inflammatory response, organismal injury, and chromatin remodeling mediated by H3K9 methylation. Furthermore, a subpopulation of stressed rats demonstrated very significantly altered gene expression and transcript isoforms, enriched for the differential expression of genes involved in the inflammatory response, including upregulation of cytokine and chemokine receptor gene expression coupled with downregulation of epithelial adherens and tight junction mRNAs. In summary, these findings support that chronic stress is associated with increased levels of cytokines and chemokines, their downstream signaling pathways coupled to dysregulation of intestinal cell development and function. Epigenetic regulation of chromatin remodeling likely plays a prominent role in this process. Results also suggest that super enhancers play a primary role in chronic stress-associated intestinal barrier dysfunction.

βIII-Tubulin Gene Regulation in Health and Disease

Microtubule proteins form a dynamic component of the cytoskeleton, and play key roles in cellular processes, such as vesicular transport, cell motility and mitosis. Expression of microtubule proteins are often dysregulated in cancer. In particular, the microtubule protein βIII-tubulin, encoded by the TUBB3 gene, is aberrantly expressed in a range of epithelial tumours and is associated with drug resistance and aggressive disease. In normal cells, TUBB3 expression is tightly restricted, and is found almost exclusively in neuronal and testicular tissues. Understanding the mechanisms that control TUBB3 expression, both in cancer, mature and developing tissues will help to unravel the basic biology of the protein, its role in cancer, and may ultimately lead to the development of new therapeutic approaches to target this protein. This review is devoted to the transcriptional and posttranscriptional regulation of TUBB3 in normal and cancerous tissue.

Novel MicroRNA-Regulated Transcript Networks Are Associated with Chemotherapy Response in Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic cancer, in part due to resistance to platinum-based chemotherapy reported among 20% of patients. This study aims to generate novel hypotheses of the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. Differential expression analyses of mRNA- and microRNA-sequencing data from HGSOC patients of The Cancer Genome Atlas identified 21 microRNAs associated with angiogenesis and 196 mRNAs enriched for adaptive immunity and translation. Coexpression network analysis identified three microRNA networks associated with chemotherapy response enriched for lipoprotein transport and oncogenic pathways, as well as two mRNA networks enriched for ubiquitination and lipid metabolism. These network modules were replicated in two independent ovarian cancer cohorts. Moreover, integrative analyses of the mRNA/microRNA sequencing and single-nucleotide polymorphisms (SNPs) revealed potential regulation of significant mRNA transcripts by microRNAs and SNPs (expression quantitative trait loci). Thus, we report novel transcriptional networks and biological pathways associated with resistance to platinum-based chemotherapy in HGSOC patients. These results expand our understanding of the effector networks and regulators of chemotherapy response, which will help to improve the management of ovarian cancer.

Software evaluation for de novo detection of transposons

Transposable elements (TEs) are major genomic components in most eukaryotic genomes and play an important role in genome evolution. However, despite their relevance the identification of TEs is not an easy task and a number of tools were developed to tackle this problem. To better understand how they perform, we tested several widely used tools for de novo TE detection and compared their performance on both simulated data and well curated genomic sequences. As expected, tools that build TE-models performed better than k-mer counting ones, with RepeatModeler beating competitors in most datasets. However, there is a tendency for most tools to identify TE-regions in a fragmented manner and it is also frequent that small TEs or fragmented TEs are not detected. Consequently, the identification of TEs is still a challenging endeavor and it requires a significant manual curation by an experienced expert. The results will be helpful for identifying common issues associated with TE-annotation and for evaluating how comparable are the results obtained with different tools.

An NF-κB- and Therapy-Related Regulatory Network in Glioma: A Potential Mechanism of Action for Natural Antiglioma Agents

High-grade gliomas are among the most aggressive malignancies, with significantly low median survival. Recent experimental research in the field has highlighted the importance of natural substances as possible antiglioma agents, also known for their antioxidant and anti-inflammatory action. We have previously shown that natural substances target several surface cluster of differentiation (CD) markers in glioma cells, as part of their mechanism of action. We analyzed the genome-wide NF-κB binding sites residing in consensus regulatory elements, based on ENCODE data. We found that NF-κB binding sites reside adjacent to the promoter regions of genes encoding CD markers targeted by antiglioma agents (namely, CD15/FUT4, CD28, CD44, CD58, CD61/SELL, CD71/TFRC, and CD122/IL2RB). Network and pathway analysis revealed that the markers are associated with a core network of genes that, altogether, participate in processes that associate tumorigenesis with inflammation and immune evasion. Our results reveal a core regulatory network that can be targeted in glioblastoma, with apparent implications in individuals that suffer from this devastating malignancy.

Duplications disrupt chromatin architecture and rewire GPR101-enhancer communication in X-linked acrogigantism

X-linked acrogigantism (X-LAG) is the most severe form of pituitary gigantism and is characterized by aggressive growth hormone (GH)-secreting pituitary tumors that occur in early childhood. X-LAG is associated with chromosome Xq26.3 duplications (the X-LAG locus typically includes VGLL1, CD40LG, ARHGEF6, RBMX, and GPR101) that lead to massive pituitary tumoral expression of GPR101, a novel regulator of GH secretion. The mechanism by which the duplications lead to marked pituitary misexpression of GPR101 alone was previously unclear. Using Hi-C and 4C-seq, we characterized the normal chromatin structure at the X-LAG locus. We showed that GPR101 is located within a topologically associating domain (TAD) delineated by a tissue-invariant border that separates it from centromeric genes and regulatory sequences. Next, using 4C-seq with GPR101, RBMX, and VGLL1 viewpoints, we showed that the duplications in multiple X-LAG-affected individuals led to ectopic interactions that crossed the invariant TAD border, indicating the existence of a similar and consistent mechanism of neo-TAD formation in X-LAG. We then identified several pituitary active cis-regulatory elements (CREs) within the neo-TAD and demonstrated in vitro that one of them significantly enhanced reporter gene expression. At the same time, we showed that the GPR101 promoter permits the incorporation of new regulatory information. Our results indicate that X-LAG is a TADopathy of the endocrine system in which Xq26.3 duplications disrupt the local chromatin architecture forming a neo-TAD. Rewiring GPR101-enhancer interaction within the new regulatory unit is likely to cause the high levels of aberrant expression of GPR101 in pituitary tumors caused by X-LAG.

A tool for feature extraction from biological sequences

With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the ability of human beings, creating a splendid opportunity for machine learning methods to grow. The methods, however, are practical only when the sequences are converted into feature vectors. Many tools target this task including iLearnPlus, a Python-based tool which supports a rich set of features. In this paper, we propose a holistic tool that extracts features from biological sequences (i.e. DNA, RNA and Protein). These features are the inputs to machine learning models that predict properties, structures or functions of the input sequences. Our tool not only supports all features in iLearnPlus but also 30 additional features which exist in the literature. Moreover, our tool is based on R language which makes an alternative for bioinformaticians to transform sequences into feature vectors. We have compared the conversion time of our tool with that of iLearnPlus: we transform the sequences much faster. We convert small nucleotides by a median of 2.8X faster, while we outperform iLearnPlus by a median of 6.3X for large sequences. Finally, in amino acids, our tool achieves a median speedup of 23.9X.

Supervised promoter recognition: a benchmark framework

Motivation

Deep learning has become a prevalent method in identifying genomic regulatory sequences such as promoters. In a number of recent papers, the performance of deep learning models has continually been reported as an improvement over alternatives for sequence-based promoter recognition. However, the performance improvements in these models do not account for the different datasets that models are evaluated on. The lack of a consensus dataset and procedure for benchmarking purposes has made the comparison of each model’s true performance difficult to assess.

Results

We present a framework called Supervised Promoter Recognition Framework (‘SUPR REF’) capable of streamlining the complete process of training, validating, testing, and comparing promoter recognition models in a systematic manner. SUPR REF includes the creation of biologically relevant benchmark datasets to be used in the evaluation process of deep learning promoter recognition models. We showcase this framework by comparing the models’ performances on alternative datasets, and properly evaluate previously published models on new benchmark datasets. Our results show that the reliability of deep learning ab initio promoter recognition models on eukaryotic genomic sequences is still not at a sufficient level, as overall performance is still low. These results originate from a subset of promoters, the well-known RNA Polymerase II core promoters. Furthermore, given the observational nature of these data, cross-validation results from small promoter datasets need to be interpreted with caution.

Advancing discovery in hearing research via biologist-friendly access to multi-omic data

High-throughput cell type-specific multi-omic analyses have advanced our understanding of inner ear biology in an unprecedented way. The full benefit of these data, however, is reached from their re-use. Successful re-use of data requires identifying the natural users and ensuring proper data democratization and federation for their seamless and meaningful access. Here we discuss universal challenges in access and re-use of multi-omic data, possible solutions, and introduce the gEAR (the gene Expression Analysis Resource, umgear.org)-a tool for multi-omic data visualization, sharing and access for the ear field.

Integrative Analyses of Circulating mRNA and lncRNA Expression Profile in Plasma of Lung Cancer Patients

Circulating-free RNAs (cfRNAs) have been regarded as potential biomarkers for "liquid biopsy" in cancers. However, the circulating messenger RNA (mRNA) and long noncoding RNA (lncRNA) profiles of lung cancer have not been fully characterized. In this study, we profiled circulating mRNA and lncRNA profiles of 16 lung cancer patients and 4 patients with benign pulmonary nodules. Compared with benign pulmonary nodules, 806 mRNAs and 1,762 lncRNAs were differentially expressed in plasma of lung adenocarcinoma patients. For lung squamous cell carcinomas, 256 mRNAs and 946 lncRNAs were differentially expressed. A total of 231 mRNAs and 298 lncRNAs were differentially expressed in small cell lung cancer. Eleven mRNAs, 51 lncRNAs, and 207 canonical pathways were differentially expressed in lung cancer in total. Forty-five blood samples were collected to verify our findings via performing qPCR. There are plenty of meaningful mRNAs and lncRNAs that were found. MYC, a transcription regulator associated with the stemness of cancer cells, is overexpressed in lung adenocarcinoma. Transforming growth factor beta (TGFB1), which plays pleiotropic roles in cancer progression, was found to be upregulated in lung squamous carcinoma. MALAT1, a well-known oncogenic lncRNA, was also found to be upregulated in lung squamous carcinoma. Thus, this study provided a systematic resource of mRNA and lncRNA expression profiles in lung cancer plasma.

Fine Mapping of the MAP2K5 Region Identified rs7175517 as a Causal Variant Related to BMI in China and the United Kingdom Populations

Background: Genome-wide association studies (GWASs) have consistently identified MAP2K5 as an obesity susceptibility gene. To deepen our understanding of the potential causal genetic variants of this region, a fine-mapping study of MAP2K5 was conducted.

Methods and Results: SNPs rs7175517 (G > A) and rs4776970 (T > A) were identified as the leading SNPs associated with BMI in both Chinese and the United Kingdom populations. Second, colocalization of GWAS and expression quantitative trait loci (eQTL) analyses and bioinformatic analyses indicated that rs7175517 is the functionally leading variant in the MAP2K5 gene region. Dual-luciferase assays indicated that the G allele of rs7175517 reduced the mRNA expression of MAP2K5 in HEK293T cells. The possible mechanism was that the G allele interacted with more RNA repressors from nuclei extracts, which was evidenced by electrophoretic mobility shift assays (EMSAs). Furthermore, the pathway enrichment analyses of the products from DNA pull-down and protein mass spectrometry demonstrated that the G allele of rs7175517 might interact with RNA catabolic or splicing transcription factors, which consequentially increased adiposity deposition.

Conclusion: SNP rs7175517 of the MAP2K5 gene was the putative causal variant associated with BMI. More precisely designed in vitro or animal experiments are warranted to further delineate the function of MAP2K5 in adipogenesis.

KLF15 cistromes reveal a hepatocyte pathway governing plasma corticosteroid transport and systemic inflammation

Circulating corticosteroids orchestrate stress adaptation, including inhibition of inflammation. While pathways governing corticosteroid biosynthesis and intracellular signaling are well understood, less is known about mechanisms controlling plasma corticosteroid transport. Here, we show that hepatocyte KLF15 (Kruppel-like factor 15) controls plasma corticosteroid transport and inflammatory responses through direct transcriptional activation of Serpina6, which encodes corticosteroid-binding globulin (CBG). Klf15-deficient mice have profoundly low CBG, reduced plasma corticosteroid binding capacity, and heightened mortality during inflammatory stress. These defects are completely rescued by reconstituting CBG, supporting that KLF15 works primarily through CBG to control plasma corticosterone homeostasis. To understand transcriptional mechanisms, we generated the first KLF15 cistromes using newly engineered Klf153xFLAG mice. Unexpectedly, liver KLF15 is predominantly promoter enriched, including Serpina6, where it binds a palindromic GC-rich motif, opens chromatin, and transactivates genes with minimal associated direct gene repression. Overall, we provide critical mechanistic insight into KLF15 function and identify a hepatocyte-intrinsic transcriptional module that potently regulates systemic corticosteroid transport and inflammation.

A comparison on predicting functional impact of genomic variants

Single-nucleotide polymorphism (SNPs) may cause the diverse functional impact on RNA or protein changing genotype and phenotype, which may lead to common or complex diseases like cancers. Accurate prediction of the functional impact of SNPs is crucial to discover the 'influential' (deleterious, pathogenic, disease-causing, and predisposing) variants from massive background polymorphisms in the human genome. Increasing computational methods have been developed to predict the functional impact of variants. However, predictive performances of these computational methods on massive genomic variants are still unclear. In this regard, we systematically evaluated 14 important computational methods including specific methods for one type of variant and general methods for multiple types of variants from several aspects; none of these methods achieved excellent (AUC ≥ 0.9) performance in both data sets. CADD and REVEL achieved excellent performance on multiple types of variants and missense variants, respectively. This comparison aims to assist researchers and clinicians to select appropriate methods or develop better predictive methods.

TET2 Regulates 5-Hydroxymethylcytosine Signature and CD4+ T-Cell Balance in Allergic Rhinitis

Purpose

Previous studies have shown the role of ten-eleven translocation 2 (TET2) in CD4⁺ T cells. However, its function in CD4⁺ T cells under allergic inflammation is unclear. We aimed to investigate the epigenomic distribution of DNA 5-hydroxymethylcytosine (5hmC) and the role of TET2 in CD4⁺ T cells of allergic rhinitis (AR).

Methods

The hMeDIP-seq was performed to identify sequences with 5hmC deposition in CD4⁺ T cells of AR patients. Tet2-deficient or wild type mice were stimulated with ovalbumin (OVA) to develop an AR mouse model. The histopathology in nasal mucosae, Th1/Th2/Treg/Th17 cell percentage, concentrations of Th-related cytokines, expression of Tet and differential hydroxymethylated genes (DhMG), and the global deposition of 5hmC in sorted CD4⁺ T cells were detected.

Results

Epigenome-wide 5hmC landscape and DhMG in the CD4⁺ T cells of AR patients were identified. Tet2 depletion did not led to spontaneous inflammation. However, under the stimulation of allergen, OVA, loss of Tet2 resulted in the exacerbation of allergic inflammation, which was characterized by severer allergic symptoms, more inflammatory cells infiltrating the nasal lamina propria, sharper imbalances between Th1/Th2 and Treg/Th17 cells, and excessive secretion of OVA-specific IgE and Th2-related cytokines. Moreover, altered mRNA production of several DhMG and sharp decrease in 5hmC deposition were also observed in Tet2-deficient OVA-exposed mice.

Conclusions

TET2 may regulate DNA 5hmC, DhMG expressions, and CD4⁺ T cell balance in AR.

Learning Useful Representations of DNA Sequences From ChIP-Seq Datasets for Exploring Transcription Factor Binding Specificities

Deep learning has been successfully applied to surprisingly different domains. Researchers and practitioners are employing trained deep learning models to enrich our knowledge. Transcription factors (TFs)are essential for regulating gene expression in all organisms by binding to specific DNA sequences. Here, we designed a deep learning model named SemanticCS (Semantic ChIP-seq)to predict TF binding specificities. We trained our learning model on an ensemble of ChIP-seq datasets (Multi-TF-cell)to learn useful intermediate features across multiple TFs and cells. To interpret these feature vectors, visualization analysis was used. Our results indicate that these learned representations can be used to train shallow machines for other tasks. Using diverse experimental data and evaluation metrics, we show that SemanticCS outperforms other popular methods. In addition, from experimental data, SemanticCS can help to identify the substitutions that cause regulatory abnormalities and to evaluate the effect of substitutions on the binding affinity for the RXR transcription factor. The online server for SemanticCS is freely available at http://qianglab.scst.suda.edu.cn/semanticCS/.

Degree of Freedom of Gene Expression in Saccharomyces cerevisiae

The complexity of genome-wide gene expression has not yet been adequately addressed due to a lack of comprehensive statistical analyses. In the present study, we introduce degree of freedom (DOF) as a summary statistic for evaluating gene expression complexity. Because DOF can be interpreted by a state-space representation, application of the DOF is highly useful for understanding gene activities. We used over 11,000 gene expression data sets to reveal that the DOF of gene expression in Saccharomyces cerevisiae is not greater than 450. We further demonstrated that various degrees of freedom of gene expression can be interpreted by different sequence motifs within promoter regions and Gene Ontology (GO) terms. The well-known TATA box is the most significant one among the identified motifs, while the GO term "ribosome genesis" is an associated biological process. On the basis of transcriptional freedom, our findings suggest that the regulation of gene expression can be modeled using only a few state variables. IMPORTANCE Yeast works like a well-organized factory. Each of its components works in its own way, while affecting the activities of others. The order of all activities is largely governed by the regulation of gene expression. In recent decades, biologists have recognized many regulations for yeast genes. However, it is not known how closely the regulation links each gene together to make all components of the cell work as a whole. In other words, biologists are very interested in how many independent control factors are needed to operate an artificial "cell" that works the same as a real one. In this work, we suggested that only 450 control factors were sufficient to represent the regulation of all 5800 yeast genes.

Gestational Perfluoroalkyl Substance Exposure and DNA Methylation at Birth and 12 Years of Age: A Longitudinal Epigenome-Wide Association Study

BACKGROUND

DNA methylation alterations may underlie associations between gestational perfluoroalkyl substances (PFAS) exposure and later-life health outcomes. To the best of our knowledge, no longitudinal studies have examined the associations between gestational PFAS and DNA methylation.

OBJECTIVES

We examined associations of gestational PFAS exposure with longitudinal DNA methylation measures at birth and in adolescence using the Health Outcomes and Measures of the Environment (HOME) Study (2003-2006; Cincinnati, Ohio).

METHODS

We quantified serum concentrations of perfluorooctanoate (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoate (PFNA), and perfluorohexane sulfonate (PFHxS) in mothers during pregnancy. We measured DNA methylation in cord blood (n=266) and peripheral leukocytes at 12 years of age (n=160) using the Illumina HumanMethylation EPIC BeadChip. We analyzed associations between log2-transformed PFAS concentrations and repeated DNA methylation measures using linear regression with generalized estimating equations. We included interaction terms between children's age and gestational PFAS. We performed Gene Ontology enrichment analysis to identify molecular pathways. We used Project Viva (1999-2002; Boston, Massachusetts) to replicate significant associations.

RESULTS

After adjusting for covariates, 435 cytosine-guanine dinucleotide (CpG) sites were associated with PFAS (false discovery rate, q<0.05). Specifically, we identified 2 CpGs for PFOS, 12 for PFOA, 8 for PFHxS, and 413 for PFNA; none overlapped. Among these, 2 CpGs for PFOA and 4 for PFNA were replicated in Project Viva. Some of the PFAS-associated CpG sites annotated to gene regions related to cancers, cognitive health, cardiovascular disease, and kidney function. We found little evidence that the associations between PFAS and DNA methylation differed by children's age.

DISCUSSION

In these longitudinal data, PFAS biomarkers were associated with differences in several CpGs at birth and at 12 years of age in or near genes linked to some PFAS-associated health outcomes. Future studies should examine whether DNA methylation mediates associations between gestational PFAS exposure and health. https://doi.org/10.1289/EHP10118.

Integrating whole genome sequencing, methylation, gene expression, topological associated domain information in regulatory mutation prediction: a study of follicular lymphoma

A major challenge in human genetics is of the analysis of the interplay between genetic and epigenetic factors in a multifactorial disease like cancer. Here, a novel methodology is proposed to investigate genome-wide regulatory mechanisms in cancer, as studied with the example of follicular Lymphoma (FL). In a first phase, a new machine-learning method is designed to identify Differentially Methylated Regions (DMRs) by computing six attributes. In a second phase, an integrative data analysis method is developed to study regulatory mutations in FL, by considering differential methylation information together with DNA sequence variation, differential gene expression, 3D organization of genome (e.g., topologically associated domains), and enriched biological pathways. Resulting mutation block-gene pairs are further ranked to find out the significant ones. By this approach, BCL2 and BCL6 were identified as top-ranking FL-related genes with several mutation blocks and DMRs acting on their regulatory regions. Two additional genes, CDCA4 and CTSO, were also found in top rank with significant DNA sequence variation and differential methylation in neighboring areas, pointing towards their potential use as biomarkers for FL. This work combines both genomic and epigenomic information to investigate genome-wide gene regulatory mechanisms in cancer and contribute to devising novel treatment strategies.

Methylome and Transcriptome-Based Integration Analysis Identified Molecular Signatures Associated With Meningitis Induced by Glaesserella parasuis

Glaesserella parasuis (G. parasuis) can elicit serious inflammatory responses and cause meningitis in piglets. Previous epigenetic studies have indicated that alterations in host DNA methylation may modify the inflammatory response to bacterial infection. However, to date, genome-wide analysis of the DNA methylome during meningitis caused by G. parasuis infection is still lacking. In this study, we employed an unbiased approach using deep sequencing to profile the DNA methylome and transcriptome from G. parasuis infected porcine brain (cerebrum) and integrated the data to identify key differential methylation regions/sites involved in the regulation of the inflammatory response. Results showed that DNA methylation patterns and gene expression profiles from porcine brain were changed after G. parasuis infection. The majority of the altered DNA methylation regions were found in the intergenic regions and introns and not associated with CpG islands, with only a low percentage occurring at promoter or exon regions. Integrated analysis of the DNA methylome and transcriptome identified a number of inversely and positively correlated genes between DNA methylation and gene expression, following the criteria of |log₂FC| > 0.5, |diffMethy| > 0.1, and P < 0.05. Differential expression and methylation of two significant genes, semaphoring 4D (SEMA4D) and von Willebrand factor A domain containing 1 (VWA1), were validated by qRT-PCR and bisulfite sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses demonstrated that DNA methylation inversely correlated genes in G. parasuis infected porcine brains were mainly involved with cell adhesion molecules (CAMs), bacterial invasion of epithelial cells, RIG-1-like receptor signaling pathways, and hematopoietic cell lineage signaling pathways. In addition, a protein-protein interaction network of differentially methylated genes found potential candidate molecular interactions relevant to the pathology of G. parasuis infection. To the best of our knowledge, this is the first attempt to integrate the DNA methylome and transcriptome data from G. parasuis infected porcine brains. Our findings will help understanding the contribution of genome-wide DNA methylation to the pathogenesis of meningitis in pigs and developing epigenetic biomarkers and therapeutic targets for the treatment of G. parasuis induced meningitis.

Monitoring Clinical-Pathological Grading of Hepatocellular Carcinoma Using MicroRNA-Guided Semiconducting Polymer Dots

MicroRNAs (miRNAs) are a class of small, noncoding RNAs involved in nearly all genetic central dogma processes and human biological behavior, which also play a significant role in the pathological activity of tumors, such as gene transcription, protein translation, and exosome secretion. Therefore, through the navigation of certain specific miRNAs, we can trace the specific physiological processes or image some specific tissues. Designing and accurately positioning microRNA (miRNA)-sensitive fluorescent nanoprobes with benign specificity and recognition in cells or tissues are a challenging research field. To solve the difficulties, we introduce four semiconducting polymer dots (Pdots) as nanoprobes linked by specific miRNA antisense sequences for monitoring the pathological grading by the variation in miRNA expression. Based on the base pairing principle, these miRNA-sensitive Pdots could bind to specific miRNAs within the cancerous cells. As impacted by the background of different pathology gradings, the proportions of the four hepatocellular carcinoma (HCC)-specific miRNAs within the cancerous cell are different, and the pathological grading of the patient tissues can be determined by comparing the palette combinations. The short single-stranded RNA-functionalized Pdots, which have excellent microRNA sensitivity, are observed in an experimental cell model and a series of tissue specimens from HCC patients for the first time. Using the Förster (or fluorescence) resonance energy transfer (FRET) model of Pdots and Cy3dt tag to simulate in vivo miRNA detection, the superior sensitivity and specificity of these nanoprobes are verified. The interference of subjective factors in traditional single/bis-dye emission intensity detection is abandoned, and multiple label staining is used to enhance sensitivity further and reduce the false-positive rate. The feasibility exhibited by this novel staining method is verified in normal hepatocellular HCC cell lines and 16 frozen ultrathin tissue sections, which are employed to quantify pathological grading-related color presentation systems for clinical doctors and pathologists' use. The intelligently designed miRNA-guided Pdots will emerge as an ideal platform with promising biological imaging.

Integrating external controls in case–control studies improves power for rare‐variant tests

Large-scale sequencing and genotyping data provide an opportunity to integrate external samples as controls to improve power of association tests. However, due to the systematic differences between genotyped samples from different studies, naively aggregating the controls could lead to inflation in Type I error rates. There has been recent effort to integrate external controls while adjusting for batch effect, such as the integrating External Controls into Association Test (iECAT) and its score-based single variant tests. Building on the original iECAT framework, we propose an iECAT-Score region-based test that increases power for rare-variant tests when integrating external controls. This method assesses the systematic batch effect between internal and external samples at each variant and constructs compound shrinkage score statistics to test for the joint genetic effect within a gene or a region, while adjusting for covariates and population stratification. Through simulation studies, we demonstrate that the proposed method controls for Type I error rates and improves power in rare-variant tests. The application of the proposed method to the association studies of age-related macular degeneration (AMD) from the International AMD Genomics Consortium and UK Biobank revealed novel rare-variant associations in gene DXO. Through the incorporation of external controls, the iECAT methods offer a powerful suite to identify disease-associated genetic variants, further shedding light on future directions to investigate roles of rare variants in human diseases.

A mammalian methylation array for profiling methylation levels at conserved sequences

Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures up to 36k CpGs per species that are well conserved across many mammalian species. We designed a set of probes that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development of epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others.

Dual RNA sequencing reveals dendritic cell reprogramming in response to typhoidal Salmonella invasion

Salmonella enterica represent a major disease burden worldwide. S. enterica serovar Typhi (S. Typhi) is responsible for potentially life-threatening Typhoid fever affecting 10.9 million people annually. While non-typhoidal Salmonella (NTS) serovars usually trigger self-limiting diarrhoea, invasive NTS bacteraemia is a growing public health challenge. Dendritic cells (DCs) are key professional antigen presenting cells of the human immune system. The ability of pathogenic bacteria to subvert DC functions and prevent T cell recognition contributes to their survival and dissemination within the host. Here, we adapted dual RNA-sequencing to define how different Salmonella pathovariants remodel their gene expression in tandem with that of infected DCs. We find DCs harness iron handling pathways to defend against invading Salmonellas, which S. Typhi is able to circumvent by mounting a robust response to nitrosative stress. In parallel, we uncover the alternative strategies invasive NTS employ to impair DC functions.

SMAD4 mutations do not preclude epithelial-mesenchymal transition in colorectal cancer

Transforming growth factor beta (TGFβ) superfamily signaling is a prime inducer of epithelial-mesenchymal transitions (EMT) that foster cancer cell invasion and metastasis, a major cause of cancer-related deaths. Yet, TGFβ signaling is frequently inactivated in human tumor entities including colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD) with a high proportion of mutations incapacitating SMAD4, which codes for a transcription factor (TF) central to canonical TGFβ and bone morphogenetic protein (BMP) signaling. Beyond its role in initiating EMT, SMAD4 was reported to crucially contribute to subsequent gene regulatory events during EMT execution. It is therefore widely assumed that SMAD4-mutant (SMAD4mut) cancer cells are unable to undergo EMT. Here, we scrutinized this notion and probed for potential SMAD4-independent EMT execution using SMAD4mut CRC cell lines. We show that SMAD4mut cells exhibit morphological changes, become invasive, and regulate EMT marker genes upon induction of the EMT-TF SNAIL1. Furthermore, SNAIL1-induced EMT in SMAD4mut cells was found to be entirely independent of TGFβ/BMP receptor activity. Global assessment of the SNAIL1-dependent transcriptome confirmed the manifestation of an EMT gene regulatory program in SMAD4mut cells highly related to established EMT signatures. Finally, analyses of human tumor transcriptomes showed that SMAD4 mutations are not underrepresented in mesenchymal tumor samples and that expression patterns of EMT-associated genes are similar in SMAD4mut and SMAD4 wild-type (SMAD4wt) cases. Altogether, our findings suggest that alternative TFs take over the gene regulatory functions of SMAD4 downstream of EMT-TFs, arguing for considerable plasticity of gene regulatory networks operating in EMT execution. Further, they establish that EMT is not categorically precluded in SMAD4mut tumors, which is relevant for their diagnostic and therapeutic evaluation.

Chromosome-level genome of Schistosoma haematobium underpins genome-wide explorations of molecular variation

Urogenital schistosomiasis is caused by the blood fluke Schistosoma haematobium and is one of the most neglected tropical diseases worldwide, afflicting > 100 million people. It is characterised by granulomata, fibrosis and calcification in urogenital tissues, and can lead to increased susceptibility to HIV/AIDS and squamous cell carcinoma of the bladder. To complement available treatment programs and break the transmission of disease, sound knowledge and understanding of the biology and ecology of S. haematobium is required. Hybridisation/introgression events and molecular variation among members of the S. haematobium-group might effect important biological and/or disease traits as well as the morbidity of disease and the effectiveness of control programs including mass drug administration. Here we report the first chromosome-contiguous genome for a well-defined laboratory line of this blood fluke. An exploration of this genome using transcriptomic data for all key developmental stages allowed us to refine gene models (including non-coding elements) and annotations, discover 'new' genes and transcription profiles for these stages, likely linked to development and/or pathogenesis. Molecular variation within S. haematobium among some geographical locations in Africa revealed unique genomic 'signatures' that matched species other than S. haematobium, indicating the occurrence of introgression events. The present reference genome (designated Shae.V3) and the findings from this study solidly underpin future functional genomic and molecular investigations of S. haematobium and accelerate systematic, large-scale population genomics investigations, with a focus on improved and sustained control of urogenital schistosomiasis.

Identification and validation of CALCRL-associated prognostic genes in acute myeloid leukemia

In the past few decades, several advances have been made in the field of acute myeloid leukemia (AML), especially in the development of novel drugs. However, the overall survival rate remains particularly disappointing due to a high rate of chemotherapy resistance and relapse. The calcitonin receptor-like receptor (CALCRL) is a novel promising therapeutic target of AML and has been indicated to be strongly correlated with chemotherapy resistance and relapse driven by leukemic stem cells. Nevertheless, the CALCRL downstream genes associated with the drug resistance and relapse of AML remain to be elucidated. Within this study, we used multiple gene expression datasets from the Gene Expression Omnibus (GEO) database and cBioPortal to explore the candidate CALCRL-associated genes that could potentially mediate the chemoresistance and relapse of AML. Then, we investigated the prognostic value, coexpression relationship with CALCRL, and expression characteristics of these genes using independent data from The Cancer Genome Atlas (TCGA). Eventually, three genes were screened out as CALCRL-associated prognostic genes. The expression of AGTPBP1 and LYST was negatively correlated with CALCRL, high expression of which was associated with favorable prognosis in AML. In contrast, the expression of ETS2 was positively correlated with CALCRL, high expression of which was associated with poor prognosis in AML. The results indicated that the three prognostic genes are potential CALCRL downstream genes that mediate drug resistance and relapse in AML. This study helps to further explore the role and molecular pathways of CALCRL in mediating drug resistance and relapse of AML.

Interferon regulatory factor-1 regulates cisplatin-induced apoptosis and autophagy in A549 lung cancer cells

This study aimed to investigate the expression and function of interferon regulatory factor-1 (IRF-1) in non-small cell lung cancer (NSCLC). IRF-1 expression and its prognostic value were investigated through bioinformatic analysis. The protein expression levels of IRF-1, cleaved caspase 3, and LC3-I/II were analyzed by western blotting. A lentiviral vector was used to overexpress or knockdown IRF-1 in vitro. Mitochondrial membrane potential (MMP) and reactive oxygen species (ROS) were analyzed by JC-1 and DCFH-DA staining, respectively. ATP, SOD, MDA, cell viability, LDH release, and caspase 3 activity were evaluated using commercial kits. Compared to the levels in normal tissues, IRF-1 expression was significantly lower in lung cancer tissues and was a prognostic factor for NSCLC. Cisplatin treatment-induced IRF-1 activation, ROS production, ATP depletion, SOD consumption, and MDA accumulation in A549 lung cancer cells. IRF-1 overexpression promoted mitochondrial depolarization, oxidative stress, and apoptotic cell death and inhibited autophagy in A549 cells, and these effects could be reversed by IRF-1 knockdown. These data suggest that IRF-1 regulates apoptosis, autophagy and oxidative stress, which might be served as a potential target for increasing chemotherapy sensitivity of lung cancer.

Prediction and analysis of functional RNA structures within the integrative genomics viewer

In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results—in conjunction with probing data and other annotations—in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast ‘omics’ resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

miR-130b Expression Level Changes Promote Cervical Cancer Cell Proliferation But Inhibit its Apoptosis by Targeting CDKN1A Gene

Background: Dysregulation of miR-130b expression is associated with the development of different cancers. However, the description of the biological roles of miR-130b in the growth and survival of cervical cancer cells is limited.

Methods: The miR-130b levels in cervical cancer cells during different stages of growth were determined using reverse transcription-quantitative PCR. The methylation level of DNA sequences upstream of the miR-130b gene was measured using an SYBR Green-based quantitative methylation-specific PCR. Reverse transcription-quantitative PCR, Western blotting, and fluorescence report assays were used to identify the miR-130b-targeted gene. Cell counting kit-8 and comet assays were used to determine cell viability and DNA damage levels in cells, respectively. EdU Apopllo488 in vitro Flow Cytometry kit, propidium iodide staining, anti-γ-H2AX antibody staining, and Annexin-V apoptosis kit were subsequently used to determine DNA synthesis rates, cell cycle distribution, count of DNA double-strand breaks, and levels of apoptotic cells.

Results: miR-130b levels increased at exponential phases of the growth of cervical cancer cells but reduced at stationary phases. The methylation of a prominent CpG island near the transcript start site suppressed the miR-130b gene expression. MiR-130b increased cell viability, promoted both DNA synthesis and G1 to S phase transition of the cells at exponential phases, but reduced cell viability accompanied by accumulations of DNA breaks and augmentations in apoptosis rates of the cells in stationary phases by targeting cyclin-dependent kinase inhibitor 1A mRNA.

Conclusion: miR-130b promoted the growth of cervical cancer cells during the exponential phase, whereas it impaired the survival of cells during stationary phases.

m5C-Atlas: a comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome

5-Methylcytosine (m5C) is one of the most prevalent covalent modifications on RNA. It is known to regulate a broad variety of RNA functions, including nuclear export, RNA stability and translation. Here, we present m5C-Atlas, a database for comprehensive collection and annotation of RNA 5-methylcytosine. The database contains 166 540 m5C sites in 13 species identified from 5 base-resolution epitranscriptome profiling technologies. Moreover, condition-specific methylation levels are quantified from 351 RNA bisulfite sequencing samples gathered from 22 different studies via an integrative pipeline. The database also presents several novel features, such as the evolutionary conservation of a m5C locus, its association with SNPs, and any relevance to RNA secondary structure. All m5C-atlas data are accessible through a user-friendly interface, in which the m5C epitranscriptomes can be freely explored, shared, and annotated with putative post-transcriptional mechanisms (e.g. RBP intermolecular interaction with RNA, microRNA interaction and splicing sites). Together, these resources offer unprecedented opportunities for exploring m5C epitranscriptomes. The m5C-Atlas database is freely accessible at https://www.xjtlu.edu.cn/biologicalsciences/m5c-atlas.

TcoFBase: a comprehensive database for decoding the regulatory transcription co-factors in human and mouse

Transcription co-factors (TcoFs) play crucial roles in gene expression regulation by communicating regulatory cues from enhancers to promoters. With the rapid accumulation of TcoF associated chromatin immunoprecipitation sequencing (ChIP-seq) data, the comprehensive collection and integrative analyses of these data are urgently required. Here, we developed the TcoFBase database (http://tcof.liclab.net/TcoFbase), which aimed to document a large number of available resources for mammalian TcoFs and provided annotations and enrichment analyses of TcoFs. TcoFBase curated 2322 TcoFs and 6759 TcoFs associated ChIP-seq data from over 500 tissues/cell types in human and mouse. Importantly, TcoFBase provided detailed and abundant (epi) genetic annotations of ChIP-seq based TcoF binding regions. Furthermore, TcoFBase supported regulatory annotation information and various functional annotations for TcoFs. Meanwhile, TcoFBase embedded five types of TcoF regulatory analyses for users, including TcoF gene set enrichment, TcoF binding genomic region annotation, TcoF regulatory network analysis, TcoF-TF co-occupancy analysis and TcoF regulatory axis analysis. TcoFBase was designed to be a useful resource that will help reveal the potential biological effects of TcoFs and elucidate TcoF-related regulatory mechanisms.

Primary and Secondary Microcephaly, Global Developmental Delay, and Seizure in Two Siblings Caused by a Novel Missense Variant in the ZNF335 Gene

Autosomal recessive microcephaly is a rare clinical condition, which is characterized by reduced brain size that can be associated with delayed intellectual ability, developmental delay, and seizure. In this study, we describe two siblings with microcephaly: a 12-year-old girl with primary microcephaly, and a 7-year-old boy with secondary microcephaly, whose episodes of seizure and neurodevelopmental regression started at 6 years and 6 months of age, respectively. The interesting finding in these siblings was two different presentations of the same variant: one case with primary and one case with secondary microcephaly. Whole-exome sequencing was performed in order to identify causative variants in one family having two affected siblings with microcephaly. Confirmation of the identified variant in the ZNF335 gene in the proband and her affected brother and segregation analysis in the family were performed using the Sanger sequencing method. In both patients, a novel homozygous missense variant, [NM_022095.4: c.3346G>A; p.(Gly1116Arg)], in the ZNF335 gene was identified. The p.(Gly1116Arg) variant causes a defect in the last zinc finger domain of the protein. Conservation analysis by ConSurf server and UCSC genome browser revealed that Gly1116 is a highly conserved amino acid among different species. Different in-silico prediction tools and bioinformatics analysis predicted this variant as damaging.

An Aggregation Method to Identify the RNA Meta-Stable Secondary Structure and its Functionally Interpretable Structure Ensemble

RNA can provide vital cellular functions through its secondary or tertiary structure. Due to the low-throughput nature of experimental approaches, studies on RNA structures mainly resort to computational methods. However, current existing tools fail to consider RNA structure ensembles and do not provide ways to decipher functional hypotheses for the new predictions. In this research, a novel method was proposed to identify the functionally interpretable structure ensemble of a given RNA sequence and provide the meta-stable structure, or the most frequently observed functional RNA cellular conformation, based on the ensemble. In the prediction of meta-stable structures, the proposed method outperformed existing tools on a yeast test set. The inferred functional aspects were then manually checked and demonstrated a micro-averaging F1 value of 0.92. Further, a biological example of the yeast ASH1-E1 element was discussed to articulate that these functional aspects can also suggest testable hypotheses. Then the proposed method was verified to be well applicable to other species through a human test set. Finally, the proposed method was demonstrated to show resistance to sequence length-dependent performance deterioration.

OUP accepted manuscript

Conflicts between transcription and replication machinery are a potent source of replication stress and genome instability; however, no technique currently exists to identify endogenous genomic locations prone to transcription–replication interactions. Here, we report a novel method to identify genomic loci prone to transcription–replication interactions termed transcription–replication immunoprecipitation on nascent DNA sequencing, TRIPn-Seq. TRIPn-Seq employs the sequential immunoprecipitation of RNA polymerase 2 phosphorylated at serine 5 (RNAP2s5) followed by enrichment of nascent DNA previously labeled with bromodeoxyuridine. Using TRIPn-Seq, we mapped 1009 unique transcription–replication interactions (TRIs) in mouse primary B cells characterized by a bimodal pattern of RNAP2s5, bidirectional transcription, an enrichment of RNA:DNA hybrids, and a high probability of forming G-quadruplexes. TRIs are highly enriched at transcription start sites and map to early replicating regions. TRIs exhibit enhanced Replication Protein A association and TRI-associated genes exhibit higher replication fork termination than control transcription start sites, two marks of replication stress. TRIs colocalize with double-strand DNA breaks, are enriched for deletions, and accumulate mutations in tumors. We propose that replication stress at TRIs induces mutations potentially contributing to age-related disease, as well as tumor formation and development.

RefSeq Functional Elements as experimentally assayed nongenic reference standards and functional interactions in human and mouse

Eukaryotic genomes contain many nongenic elements that function in gene regulation, chromosome organization, recombination, repair, or replication, and mutation of those elements can affect genome function and cause disease. Although numerous epigenomic studies provide high coverage of gene regulatory regions, those data are not usually exposed in traditional genome annotation and can be difficult to access and interpret without field-specific expertise. The National Center for Biotechnology Information (NCBI) therefore provides RefSeq Functional Elements (RefSeqFEs), which represent experimentally validated human and mouse nongenic elements derived from the literature. The curated data set is comprised of richly annotated sequence records, descriptive records in the NCBI Gene database, reference genome feature annotation, and activity-based interactions between nongenic regions, target genes, and each other. The data set provides succinct functional details and transparent experimental evidence, leverages data from multiple experimental sources, is readily accessible and adaptable, and uses a flexible data model. The data have multiple uses for basic functional discovery, bioinformatics studies, genetic variant interpretation; as known positive controls for epigenomic data evaluation; and as reference standards for functional interactions. Comparisons to other gene regulatory data sets show that the RefSeqFE data set includes a wider range of feature types representing more areas of biology, but it is comparatively smaller and subject to data selection biases. RefSeqFEs thus provide an alternative and complementary resource for experimentally assayed functional elements, with future data set growth expected.

An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches

It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites in promoters is therefore a crucial step in further understanding of the relationship between promoter methylation and the regulation of mRNA gene expression. High throughput identification of DNA 5mC in wet lab is still time-consuming and labor-extensive. Thus, finding the 5mC site of genome-wide DNA promoters is still an important task. We compared the effectiveness of the most popular and strong machine learning techniques namely XGBoost, Random Forest, Deep Forest, and Deep Feedforward Neural Network in predicting the 5mC sites of genome-wide DNA promoters. A feature extraction method based on k-mers embeddings learned from a language model were also applied. Overall, the performance of all the surveyed models surpassed deep learning models of the latest studies on the same dataset employing other encoding scheme. Furthermore, the best model achieved AUC scores of 0.962 on both cross-validation and independent test data. We concluded that our approach was efficient for identifying 5mC sites of promoters with high performance.

Distinct gene expression pattern of RUNX1 mutations coordinated by target repression and promoter hypermethylation in acute myeloid leukemia

Runt-related transcription factor 1 (RUNX1) is an essential regulator of normal hematopoiesis. Its dysfunction, caused by either fusions or mutations, is frequently reported in acute myeloid leukemia (AML). However, RUNX1 mutations have been largely under-explored compared with RUNX1 fusions mainly due to their elusive genetic characteristics. Here, based on 1741 patients with AML, we report a unique expression pattern associated with RUNX1 mutations in AML. This expression pattern was coordinated by target repression and promoter hypermethylation. We first reanalyzed a joint AML cohort that consisted of three public cohorts and found that RUNX1 mutations were mainly distributed in the Runt domain and almost mutually exclusive with NPM1 mutations. Then, based on RNA-seq data from The Cancer Genome Atlas AML cohort, we developed a 300-gene signature that significantly distinguished the patients with RUNX1 mutations from those with other AML subtypes. Furthermore, we explored the mechanisms underlying this signature from the transcriptional and epigenetic levels. Using chromatin immunoprecipitation sequencing data, we found that RUNX1 target genes tended to be repressed in patients with RUNX1 mutations. Through the integration of DNA methylation array data, we illustrated that hypermethylation on the promoter regions of RUNX1-regulated genes also contributed to dysregulation in RUNX1-mutated AML. This study revealed the distinct gene expression pattern of RUNX1 mutations and the underlying mechanisms in AML development.

Epigenetics of Mitochondria-Associated Genes in Striated Muscle

Striated muscle has especially large energy demands. We identified 97 genes preferentially expressed in skeletal muscle and heart, but not in aorta, and found significant enrichment for mitochondrial associations among them. We compared the epigenomic and transcriptomic profiles of the 27 genes associated with striated muscle and mitochondria. Many showed strong correlations between their tissue-specific transcription levels, and their tissue-specific promoter, enhancer, or open chromatin as well as their DNA hypomethylation. Their striated muscle-specific enhancer chromatin was inside, upstream, or downstream of the gene, throughout much of the gene as a super-enhancer (CKMT2, SLC25A4, and ACO2), or even overlapping a neighboring gene (COX6A2, COX7A1, and COQ10A). Surprisingly, the 3' end of the 1.38 Mb PRKN (PARK2) gene (involved in mitophagy and linked to juvenile Parkinson's disease) displayed skeletal muscle/myoblast-specific enhancer chromatin, a myoblast-specific antisense RNA, as well as brain-specific enhancer chromatin. We also found novel tissue-specific RNAs in brain and embryonic stem cells within PPARGC1A (PGC-1α), which encodes a master transcriptional coregulator for mitochondrial formation and metabolism. The tissue specificity of this gene's four alternative promoters, including a muscle-associated promoter, correlated with nearby enhancer chromatin and open chromatin. Our in-depth epigenetic examination of these genes revealed previously undescribed tissue-specific enhancer chromatin, intragenic promoters, regions of DNA hypomethylation, and intragenic noncoding RNAs that give new insights into transcription control for this medically important set of genes.

Computational challenges in detection of cancer using cell-free DNA methylation

Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.

Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma

Intertumoral immune heterogeneity is a critical reason for distinct clinical benefits of immunotherapy in lung adenocarcinoma (LUAD). Tumor immunophenotype (immune 'Hot' or 'Cold') suggests immunological individual differences and potential clinical treatment guidelines. However, employing epigenome signatures to determine tumor immunophenotypes and responsive treatment is not well understood. To delineate the tumor immunophenotype and immune heterogeneity, we first distinguished the immune 'Hot' and 'Cold' tumors of LUAD based on five immune expression signatures. In terms of clinical presentation, the immune 'Hot' tumors usually had higher immunoactivity, lower disease stages and better survival outcomes than 'Cold' tumors. At the epigenome levels, we observed that distinct DNA methylation patterns between immunophenotypes were closely associated with LUAD development. Hence, we identified a set of five CpG sites as the immunophenotype-related methylation signature (iPMS) for tumor immunophenotyping and further confirmed its efficiency based on a machine learning framework. Furthermore, we found iPMS and immunophenotype-related immune checkpoints (IPCPs) could contribute to the risk of tumor progression, implying IPCP has the potential to be a novel immunotherapy blockade target. After further parsing of the role of iPMS-predicted immunophenotypes, we found immune 'Hot' was a protective factor leading to better survival outcomes when patients received the anti-PD-1/PD-L1 immunotherapy. And iPMS was also a well-performed signature (AUC = 0.752) for predicting the durable/nondurable clinical benefits. In summary, our study explored the role of epigenome signature in clinical tumor immunophenotyping. Utilizing iPMS to characterize tumor immunophenotypes will facilitate developing personalized epigenetic anticancer approaches.