Hypomorphic and dominant-negative impact of truncated SOX9 dysregulates Hedgehog-Wnt signaling, causing campomelia.

Haploinsufficiency for SOX9, the master chondrogenesis transcription factor, can underlie campomelic dysplasia (CD), an autosomal dominant skeletal malformation syndrome, because heterozygous Sox9 null mice recapitulate the bent limb (campomelia) and some other phenotypes associated with CD. However, in vitro cell assays suggest haploinsufficiency may not apply for certain mutations, notably those that truncate the protein, but in these cases in vivo evidence is lacking and underlying mechanisms are unknown. Here, using conditional mouse mutants, we compared the impact of a heterozygous Sox9 null mutation (Sox9+/-) with the Sox9+/Y440X CD mutation that truncates the C-terminal transactivation domain but spares the DNA-binding domain. While some Sox9+/Y440X mice survived, all Sox9+/- mice died perinatally. However, the skeletal defects were more severe and IHH signaling in developing limb cartilage was significantly enhanced in Sox9+/Y440X compared with Sox9+/-. Activating Sox9Y440X specifically in the chondrocyte-osteoblast lineage caused milder campomelia, and revealed cell- and noncell autonomous mechanisms acting on chondrocyte differentiation and osteogenesis in the perichondrium. Transcriptome analyses of developing Sox9+/Y440X limbs revealed dysregulated expression of genes for the extracellular matrix, as well as changes consistent with aberrant WNT and HH signaling. SOX9Y440X failed to interact with ß-catenin and was unable to suppress transactivation of Ihh in cell-based assays. We propose enhanced HH signaling in the adjacent perichondrium induces asymmetrically localized excessive perichondrial osteogenesis resulting in campomelia. Our study implicates combined haploinsufficiency/hypomorphic and dominant-negative actions of SOX9Y440X, cell-autonomous and noncell autonomous mechanisms, and dysregulated WNT and HH signaling, as the cause of human campomelia.

Accurate identification of structural variations from cancer samples.

Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the Cancer Optical Mapping for detecting Structural Variations (COMSV) method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses.

Natural small compounds comprise most cellular molecules and bind proteins as substrates, products, cofactors, and ligands. However, a large-scale investigation of in vivo protein-small metabolite interactions has not been performed. We developed a mass spectrometry assay for the large-scale identification of in vivo protein-hydrophobic small metabolite interactions in yeast and analyzed compounds that bind ergosterol biosynthetic proteins and protein kinases. Many of these proteins bind small metabolites; a few interactions were previously known, but the vast majority are new. Importantly, many key regulatory proteins such as protein kinases bind metabolites. Ergosterol was found to bind many proteins and may function as a general regulator. It is required for the activity of Ypk1, a mammalian AKT/SGK kinase homolog. Our study defines potential key regulatory steps in lipid biosynthetic pathways and suggests that small metabolites may play a more general role as regulators of protein activity and function than previously appreciated.

Quantifying full-length circular RNAs in cancer.

Circular RNAs (circRNAs) are abundantly expressed in cancer. Their resistance to exonucleases enables them to have potentially stable interactions with different types of biomolecules. Alternative splicing can create different circRNA isoforms that have different sequences and unequal interaction potentials. The study of circRNA function thus requires knowledge of complete circRNA sequences. Here we describe psirc, a method that can identify full-length circRNA isoforms and quantify their expression levels from RNA sequencing data. We confirm the effectiveness and computational efficiency of psirc using both simulated and actual experimental data. Applying psirc on transcriptome profiles from nasopharyngeal carcinoma and normal nasopharynx samples, we discover and validate circRNA isoforms differentially expressed between the two groups. Compared with the assumed circular isoforms derived from linear transcript annotations, some of the alternatively spliced circular isoforms have 100 times higher expression and contain substantially fewer microRNA response elements, showing the importance of quantifying full-length circRNA isoforms.

Whole-genome analysis of noncoding genetic variations identifies multiscale regulatory element perturbations associated with Hirschsprung disease.

It is widely recognized that noncoding genetic variants play important roles in many human diseases, but there are multiple challenges that hinder the identification of functional disease-associated noncoding variants. The number of noncoding variants can be many times that of coding variants; many of them are not functional but in linkage disequilibrium with the functional ones; different variants can have epistatic effects; different variants can affect the same genes or pathways in different individuals; and some variants are related to each other not by affecting the same gene but by affecting the binding of the same upstream regulator. To overcome these difficulties, we propose a novel analysis framework that considers convergent impacts of different genetic variants on protein binding, which provides multiscale information about disease-associated perturbations of regulatory elements, genes, and pathways. Applying it to our whole-genome sequencing data of 918 short-segment Hirschsprung disease patients and matched controls, we identify various novel genes not detected by standard single-variant and region-based tests, functionally centering on neural crest migration and development. Our framework also identifies upstream regulators whose binding is influenced by the noncoding variants. Using human neural crest cells, we confirm cell stage-specific regulatory roles of three top novel regulatory elements on our list, respectively in the RET, RASGEF1A, and PIK3C2B loci. In the PIK3C2B regulatory element, we further show that a noncoding variant found only in the patients affects the binding of the gliogenesis regulator NFIA, with a corresponding up-regulation of multiple genes in the same topologically associating domain.

Supervised enhancer prediction with epigenetic pattern recognition and targeted validation.

Enhancers are important non-coding elements, but they have traditionally been hard to characterize experimentally. The development of massively parallel assays allows the characterization of large numbers of enhancers for the first time. Here, we developed a framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features. We integrated these features with supervised machine-learning algorithms to predict enhancers. We further demonstrated that our model could be transferred to predict enhancers in mammals. We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mice and transduction-based reporter assays in human cell lines (153 enhancers in total). The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription factor binding patterns at predicted enhancers versus promoters. We demonstrated that these patterns enable the construction of a secondary model that effectively distinguishes enhancers and promoters.

Deep learning identifies and quantifies recombination hotspot determinants.

MOTIVATION: Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we propose a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes and species. RESULTS: RHSNet can significantly outperform other sequence-based methods on multiple datasets across different species, sexes and studies. In addition to being able to identify hotspot regions and the well-known determinants accurately, more importantly, RHSNet can quantify the determinants that contribute significantly to the recombination hotspot formation in the relation between PRDM9 binding motif, histone modification and GC content. Further cross-sex, cross-population and cross-species studies suggest that the proposed method has the generalization power and potential to identify and quantify the evolutionary determinant motifs. AVAILABILITY AND IMPLEMENTATION: https://github.com/frankchen121212/RHSNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate reconstruction of viral genomes in human cells from short reads using iterative refinement.

BACKGROUND: After an infection, human cells may contain viral genomes in the form of episomes or integrated DNA. Comparing the genomic sequences of different strains of a virus in human cells can often provide useful insights into its behaviour, activity and pathology, and may help develop methods for disease prevention and treatment. To support such comparative analyses, the viral genomes need to be accurately reconstructed from a large number of samples. Previous efforts either rely on customized experimental protocols or require high similarity between the sequenced genomes and a reference, both of which limit the general applicability of these approaches. In this study, we propose a pipeline, named ASPIRE, for reconstructing viral genomes accurately from short reads data of human samples, which are increasingly available from genome projects and personal genomics. ASPIRE contains a basic part that involves de novo assembly, tiling and gap filling, and additional components for iterative refinement, sequence corrections and wrapping. RESULTS: Evaluated by the alignment quality of sequencing reads to the reconstructed genomes, these additional components improve the assembly quality in general, and in some particular samples quite substantially, especially when the sequenced genome is significantly different from the reference. We use ASPIRE to reconstruct the genomes of Epstein Barr Virus (EBV) from the whole-genome sequencing data of 61 nasopharyngeal carcinoma (NPC) samples and provide these sequences as a resource for EBV research. CONCLUSIONS: ASPIRE improves the quality of the reconstructed EBV genomes in published studies and outperforms TRACESPipe in some samples considered.

New guidelines for DNA methylome studies regarding 5-hydroxymethylcytosine for understanding transcriptional regulation.

Many DNA methylome profiling methods cannot distinguish between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Because 5mC typically acts as a repressive mark whereas 5hmC is an intermediate form during active demethylation, the inability to separate their signals could lead to incorrect interpretation of the data. Is the extra information contained in 5hmC signals worth the additional experimental and computational costs? Here we combine whole-genome bisulfite sequencing (WGBS) and oxidative WGBS (oxWGBS) data in various human tissues to investigate the quantitative relationships between gene expression and the two forms of DNA methylation at promoters, transcript bodies, and immediate downstream regions. We find that 5mC and 5hmC signals correlate with gene expression in the same direction in most samples. Considering both types of signals increases the accuracy of expression levels inferred from methylation data by a median of 18.2% as compared to having only WGBS data, showing that the two forms of methylation provide complementary information about gene expression. Differential analysis between matched tumor and normal pairs is particularly affected by the superposition of 5mC and 5hmC signals in WGBS data, with at least 25%-40% of the differentially methylated regions (DMRs) identified from 5mC signals not detected from WGBS data. Our results also confirm a previous finding that methylation signals at transcript bodies are more indicative of gene expression levels than promoter methylation signals. Overall, our study provides data for evaluating the cost-effectiveness of some experimental and analysis options in the study of DNA methylation in normal and cancer samples.

Shaping the nebulous enhancer in the era of high-throughput assays and genome editing.

Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.

Identification of a major determinant for serine-threonine kinase phosphoacceptor specificity.

Eukaryotic protein kinases are generally classified as being either tyrosine or serine-threonine specific. Though not evident from inspection of their primary sequences, many serine-threonine kinases display a significant preference for serine or threonine as the phosphoacceptor residue. Here we show that a residue located in the kinase activation segment, which we term the "DFG+1" residue, acts as a major determinant for serine-threonine phosphorylation site specificity. Mutation of this residue was sufficient to switch the phosphorylation site preference for multiple kinases, including the serine-specific kinase PAK4 and the threonine-specific kinase MST4. Kinetic analysis of peptide substrate phosphorylation and crystal structures of PAK4-peptide complexes suggested that phosphoacceptor residue preference is not mediated by stronger binding of the favored substrate. Rather, favored kinase-phosphoacceptor combinations likely promote a conformation optimal for catalysis. Understanding the rules governing kinase phosphoacceptor preference allows kinases to be classified as serine or threonine specific based on their sequence.

A general near-exact k-mer counting method with low memory consumption enables de novo assembly of 106× human sequence data in 2.7 hours.

MOTIVATION: In de novo sequence assembly, a standard pre-processing step is k-mer counting, which computes the number of occurrences of every length-k sub-sequence in the sequencing reads. Sequencing errors can produce many k-mers that do not appear in the genome, leading to the need for an excessive amount of memory during counting. This issue is particularly serious when the genome to be assembled is large, the sequencing depth is high, or when the memory available is limited. RESULTS: Here, we propose a fast near-exact k-mer counting method, CQF-deNoise, which has a module for dynamically removing noisy false k-mers. It automatically determines the suitable time and number of rounds of noise removal according to a user-specified wrong removal rate. We tested CQF-deNoise comprehensively using data generated from a diverse set of genomes with various data properties, and found that the memory consumed was almost constant regardless of the sequencing errors while the noise removal procedure had minimal effects on counting accuracy. Compared with four state-of-the-art k-mer counting methods, CQF-deNoise consistently performed the best in terms of memory usage, consuming 49-76% less memory than the second best method. When counting the k-mers from a human dataset with around 60× coverage, the peak memory usage of CQF-deNoise was only 10.9 GB (gigabytes) for k = 28 and 21.5 GB for k = 55. De novo assembly of 106× human sequencing data using CQF-deNoise for k-mer counting required only 2.7 h and 90 GB peak memory. AVAILABILITY AND IMPLEMENTATION: The source codes of CQF-deNoise and SH-assembly are available at https://github.com/Christina-hshi/CQF-deNoise.git and https://github.com/Christina-hshi/SH-assembly.git, respectively, both under the BSD 3-Clause license.

Loss of tumor suppressor IGFBP4 drives epigenetic reprogramming in hepatic carcinogenesis.

Genomic sequencing of hepatocellular carcinoma (HCC) uncovers a paucity of actionable mutations, underscoring the necessity to exploit epigenetic vulnerabilities for therapeutics. In HCC, EZH2-mediated H3K27me3 represents a major oncogenic chromatin modification, but how it modulates the therapeutic vulnerability of signaling pathways remains unknown. Here, we show EZH2 acts antagonistically to AKT signaling in maintaining H3K27 methylome through epigenetic silencing of IGFBP4. ChIP-seq revealed enrichment of Ezh2/H3K27me3 at silenced loci in HBx-transgenic mouse-derived HCCs, including Igfbp4 whose down-regulation significantly correlated with EZH2 overexpression and poor survivals of HCC patients. Functional characterizations demonstrated potent growth- and invasion-suppressive functions of IGFBP4, which was associated with transcriptomic alterations leading to deregulation of multiple signaling pathways. Mechanistically, IGFBP4 stimulated AKT/EZH2 phosphorylation to abrogate H3K27me3-mediated silencing, forming a reciprocal feedback loop that suppressed core transcription factor networks (FOXA1/HNF1A/HNF4A/KLF9/NR1H4) for normal liver homeostasis. Consequently, the in vivo tumorigenicity of IGFBP4-silenced HCC cells was vulnerable to pharmacological inhibition of EZH2, but not AKT. Our study unveils chromatin regulation of a novel liver tumor suppressor IGFBP4, which constitutes an AKT-EZH2 reciprocal loop in driving H3K27me3-mediated epigenetic reprogramming. Defining the aberrant chromatin landscape of HCC sheds light into the mechanistic basis of effective EZH2-targeted inhibition.

A network approach to exploring the functional basis of gene-gene epistatic interactions in disease susceptibility.

Motivation: Individual genetic variants explain only a small fraction of heritability in some diseases. Some variants have weak marginal effects on disease risk, but their joint effects are significantly stronger when occurring together. Most studies on such epistatic interactions have focused on methods for identifying the interactions and interpreting individual cases, but few have explored their general functional basis. This was due to the lack of a comprehensive list of epistatic interactions and uncertainties in associating variants to genes. Results: We conducted a large-scale survey of published research articles to compile the first comprehensive list of epistatic interactions in human diseases with detailed annotations. We used various methods to associate these variants to genes to ensure robustness. We found that these genes are significantly more connected in protein interaction networks, are more co-expressed and participate more often in the same pathways. We demonstrate using the list to discover novel disease pathways. Contact: kevinyip@cse.cuhk.edu.hk. Supplementary information: Supplementary data are available at Bioinformatics online.

A comprehensive web tool for toehold switch design.

Motivation: Toehold switches are a class of RNAs with a hairpin loop that can be unfolded upon binding a trigger RNA, thereby exposing a ribosome binding site (RBS) and permitting translation of the reporter protein. They have been shown very useful in detecting a variety of targets including RNAs from Zika and Ebola viruses. The base complementation between the toehold switch and the trigger RNA also makes it sensitive to sequence variations. Design of toehold switches involves a series of considerations related to their sequence properties, structures and specificities. Results: Here we present the first comprehensive web tool for designing toehold switches. We also propose a score for predicting the efficacy of designed toehold switches based on properties learned from ∼180 experimentally tested switches. Availability and implementation: The toehold switch web tool is available at https://yiplab.cse.cuhk.edu.hk/toehold/.

EBV-encoded miRNAs target ATM-mediated response in nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) is a highly invasive epithelial malignancy that is prevalent in southern China and Southeast Asia. It is consistently associated with latent Epstein-Barr virus (EBV) infection. In NPC, miR-BARTs, the EBV-encoded miRNAs derived from BamH1-A rightward transcripts, are abundantly expressed and contribute to cancer development by targeting various cellular and viral genes. In this study, we establish a comprehensive transcriptional profile of EBV-encoded miRNAs in a panel of NPC patient-derived xenografts and an EBV-positive NPC cell line by small RNA sequencing. Among the 40 miR-BARTs, predominant expression of 22 miRNAs was consistently detected in these tumors. Among the abundantly expressed EBV-miRNAs, BART5-5p, BART7-3p, BART9-3p, and BART14-3p could negatively regulate the expression of a key DNA double-strand break (DSB) repair gene, ataxia telangiectasia mutated (ATM), by binding to multiple sites on its 3'-UTR. Notably, the expression of these four miR-BARTs represented more than 10% of all EBV-encoded miRNAs in tumor cells, while downregulation of ATM expression was commonly detected in all of our tested sequenced samples. In addition, downregulation of ATM was also observed in primary NPC tissues in both qRT-PCR (16 NP and 45 NPC cases) and immunohistochemical staining (35 NP and 46 NPC cases) analysis. Modulation of ATM expression by BART5-5p, BART7-3p, BART9-3p, and BART14-3p was demonstrated in the transient transfection assays. These findings suggest that EBV uses miRNA machinery as a key mechanism to control the ATM signaling pathway in NPC cells. By suppressing these endogenous miR-BARTs in EBV-positive NPC cells, we further demonstrated the novel function of miR-BARTs in inhibiting Zta-induced lytic reactivation. These findings imply that the four viral miRNAs work co-operatively to modulate ATM activity in response to DNA damage and to maintain viral latency, contributing to the tumorigenesis of NPC. © 2017 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Analysis of sequencing data for probing RNA secondary structures and protein-RNA binding in studying posttranscriptional regulations.

High-throughput sequencing has been used to study posttranscriptional regulations, where the identification of protein-RNA binding is a major and fast-developing sub-area, which is in turn benefited by the sequencing methods for whole-transcriptome probing of RNA secondary structures. In the study of RNA secondary structures using high-throughput sequencing, bases are modified or cleaved according to their structural features, which alter the resulting composition of sequencing reads. In the study of protein-RNA binding, methods have been proposed to immuno-precipitate (IP) protein-bound RNA transcripts in vitro or in vivo By sequencing these transcripts, the protein-RNA interactions and the binding locations can be identified. For both types of data, read counts are affected by a combination of confounding factors, including expression levels of transcripts, sequence biases, mapping errors and the probing or IP efficiency of the experimental protocols. Careful processing of the sequencing data and proper extraction of important features are fundamentally important to a successful analysis. Here we review and compare different experimental methods for probing RNA secondary structures and binding sites of RNA-binding proteins (RBPs), and the computational methods proposed for analyzing the corresponding sequencing data. We suggest how these two types of data should be integrated to study the structural properties of RBP binding sites as a systematic way to better understand posttranscriptional regulations.

OMTools: a software package for visualizing and processing optical mapping data.

SUMMARY: Optical mapping is a molecular technique capturing specific patterns of fluorescent labels along DNA molecules. It has been widely applied in assisted-scaffolding in sequence assemblies, microbial strain typing and detection of structural variations. Various computational methods have been developed to analyze optical mapping data. However, existing tools for processing and visualizing optical map data still have many shortcomings. Here, we present OMTools, an efficient and intuitive data processing and visualization suite to handle and explore large-scale optical mapping profiles. OMTools includes modules for visualization (OMView), data processing and simulation. These modules together form an accessible and convenient pipeline for optical mapping analyses. AVAILABILITY AND IMPLEMENTATION: OMTools is implemented in Java 1.8 and released under a GPL license. OMTools can be downloaded from https://github.com/aldenleung/OMTools and run on any standard desktop computer equipped with a Java virtual machine. CONTACT: kevinyip@cse.cuhk.edu.hk or tf.chan@cuhk.edu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OMBlast: alignment tool for optical mapping using a seed-and-extend approach.

Background: Optical mapping is a technique for capturing fluorescent signal patterns of long DNA molecules (in the range of 0.1­1 Mbp). Recently, it has been complementing the widely used short-read sequencing technology by assisting with scaffolding and detecting large and complex structural variations (SVs). Here, we introduce a fast, robust and accurate tool called OMBlast for aligning optical maps, the set of signal locations on the molecules generated from optical mapping. Our method is based on the seed-and-extend approach from sequence alignment, with modifications specific to optical mapping. Results: Experiments with both synthetic and our real data demonstrate that OMBlast has higher accuracy and faster mapping speed than existing alignment methods. Our tool also shows significant improvement when aligning data with SVs. Availability and Implementation: OMBlast is implemented for Java 1.7 and is released under a GPL license. OMBlast can be downloaded from https://github.com/aldenleung/OMBlast and run directly on machines equipped with a Java virtual machine. Contact: kevinyip@cse.cuhk.edu.hk and tf.chan@cuhk.edu.hk Supplementary Information: Supplementary data are available at Bioinformatics online.

Architecture of the human regulatory network derived from ENCODE data.

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.