Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes.

Developments in biotechnologies enable multi-platform data collection for functional genomic units apart from the gene. Profiling of non-coding microRNAs (miRNAs) is a valuable tool for understanding the molecular profile of the cell, both for canonical functions and malignant behavior due to complex diseases. We propose a graphical mixed-effects statistical model incorporating miRNA-gene target relationships. We implement an integrative pathway analysis that leverages measurements of miRNA activity for joint analysis with multimodal observations of gene activity including gene expression, methylation, and copy number variation. We apply our analysis to a breast cancer dataset, and consider differential activity in signaling pathways across breast tumor subtypes. We offer discussion of specific signaling pathways and the effect of miRNA integration, as well as publish an interactive data visualization to give public access to the results of our analysis.

KARR-seq reveals cellular higher-order RNA structures and RNA-RNA interactions.

RNA fate and function are affected by their structures and interactomes. However, how RNA and RNA-binding proteins (RBPs) assemble into higher-order structures and how RNA molecules may interact with each other to facilitate functions remain largely unknown. Here we present KARR-seq, which uses N3-kethoxal labeling and multifunctional chemical crosslinkers to covalently trap and determine RNA-RNA interactions and higher-order RNA structures inside cells, independent of local protein binding to RNA. KARR-seq depicts higher-order RNA structure and detects widespread intermolecular RNA-RNA interactions with high sensitivity and accuracy. Using KARR-seq, we show that translation represses mRNA compaction under native and stress conditions. We determined the higher-order RNA structures of respiratory syncytial virus (RSV) and vesicular stomatitis virus (VSV) and identified RNA-RNA interactions between the viruses and the host RNAs that potentially regulate viral replication.

Rapamycin-Induced Feedback Activation of eIF4E-EIF4A Dependent mRNA Translation in Pancreatic Cancer.

Pancreatic cancer cells adapt molecular mechanisms to activate the protein synthesis to support tumor growth. This study reports the mTOR inhibitor rapamycin's specific and genome-wide effect on mRNA translation. Using ribosome footprinting in pancreatic cancer cells that lack the expression of 4EBP1, we establish the effect of mTOR-S6-dependent mRNAs translation. Rapamycin inhibits the translation of a subset of mRNAs including p70-S6K and proteins involved in the cell cycle and cancer cell growth. In addition, we identify translation programs that are activated following mTOR inhibition. Interestingly, rapamycin treatment results in the translational activation of kinases that are involved in mTOR signaling such as p90-RSK1. We further show that phospho-AKT1 and phospho-eIF4E are upregulated following mTOR inhibition suggesting a feedback activation of translation by rapamycin. Next, targeting eIF4E and eIF4A-dependent translation by using specific eIF4A inhibitors in combination with rapamycin shows significant growth inhibition in pancreatic cancer cells. In short, we establish the specific effect of mTOR-S6 on translation in cells lacking 4EBP1 and show that mTOR inhibition leads to feedback activation of translation via AKT-RSK1-eIF4E signals. Therefore, targeting translation downstream of mTOR presents a more efficient therapeutic strategy in pancreatic cancer.

Translocation detection from Hi-C data via scan statistics.

Recent Hi-C technology enables more comprehensive chromosomal conformation research, including the detection of structural variations, especially translocations. In this paper, we formulate the interchromosomal translocation detection as a problem of scan clustering in a spatial point process. We then develop TranScan, a new translocation detection method through scan statistics with the control of false discovery. The simulation shows that TranScan is more powerful than an existing sophisticated scan clustering method, especially under strong signal situations. Evaluation of TranScan against current translocation detection methods on realistic breakpoint simulations generated from real data suggests better discriminative power under the receiver-operating characteristic curve. Power analysis also highlights TranScan's consistent outperformance when sequencing depth and heterozygosity rate is varied. Comparatively, Type I error rate is lowest when evaluated using a karyotypically normal cell line. Both the simulation and real data analysis indicate that TranScan has great potentials in interchromosomal translocation detection using Hi-C data.

Deciphering the role of RNA structure in translation efficiency.

BACKGROUND: RNA secondary structure has broad impact on the fate of RNA metabolism. The reduced stability of secondary structures near the translation initiation site/start codon of the coding region promotes the efficiency of translation in both prokaryotic and eukaryotic species. However, the inaccuracy of in silico folding and the focus on the coding region limit our understanding of the global relationship between the whole mRNA structure and translation efficiency. Leveraging high-throughput RNA structure probing data in the transcriptome, we aim to systematically investigate the role of RNA structure in regulating translation efficiency. RESULTS: Here, we analyze the influences of hundreds of sequence and structural features on translation efficiency in the mouse embryonic stem cells (mESCs) and zebrafish developmental stages. Our findings reveal that overall in vivo RNA structure has a higher relative importance in predicting translation efficiency than in vitro RNA structure in both mESCs and zebrafish. Also, RNA structures in 3' untranslated region (UTR) have much stronger influence on translation efficiency compared to those in coding regions or 5' UTR. Furthermore, strong alternation between in vitro and in vivo structures in 3' UTR are detected in highly translated mRNAs in mESCs but not zebrafish. Instead, moderate alteration between in vitro and in vivo RNA structures in the 5' UTR and proximal coding regions are detected in highly translated mRNAs in zebrafish. CONCLUSIONS: Our results suggest the openness of the 3' UTR promotes the translation efficiency in both mice and zebrafish, with the in vivo structure in 3' UTR more important in mice than in zebrafish. This reveals a novel role of RNA secondary structure on translational regulation.

Transcriptional and Translational Dynamics of Zika and Dengue Virus Infection.

Zika virus (ZIKV) and dengue virus (DENV) are members of the Flaviviridae family of RNA viruses and cause severe disease in humans. ZIKV and DENV share over 90% of their genome sequences, however, the clinical features of Zika and dengue infections are very different reflecting tropism and cellular effects. Here, we used simultaneous RNA sequencing and ribosome footprinting to define the transcriptional and translational dynamics of ZIKV and DENV infection in human neuronal progenitor cells (hNPCs). The gene expression data showed induction of aminoacyl tRNA synthetases (ARS) and the translation activating PIM1 kinase, indicating an increase in RNA translation capacity. The data also reveal activation of different cell stress responses, with ZIKV triggering a BACH1/2 redox program, and DENV activating the ATF/CHOP endoplasmic reticulum (ER) stress program. The RNA translation data highlight activation of polyamine metabolism through changes in key enzymes and their regulators. This pathway is needed for eIF5A hypusination and has been implicated in viral translation and replication. Concerning the viral RNA genomes, ribosome occupancy readily identified highly translated open reading frames and a novel upstream ORF (uORF) in the DENV genome. Together, our data highlight both the cellular stress response and the activation of RNA translation and polyamine metabolism during DENV and ZIKV infection.

Differential RNA methylation using multivariate statistical methods.

MOTIVATION: m6A methylation is a highly prevalent post-transcriptional modification in eukaryotes. MeRIP-seq or m6A-seq, which comprises immunoprecipitation of methylation fragments , is the most common method for measuring methylation signals. Existing computational tools for analyzing MeRIP-seq data sets and identifying differentially methylated genes/regions are not most optimal. They either ignore the sparsity or dependence structure of the methylation signals within a gene/region. Modeling the methylation signals using univariate distributions could also lead to high type I error rates and low sensitivity. In this paper, we propose using mean vector testing (MVT) procedures for testing differential methylation of RNA at the gene level. MVTs use a distribution-free test statistic with proven ability to control type I error even for extremely small sample sizes. We performed a comprehensive simulation study comparing the MVTs to existing MeRIP-seq data analysis tools. Comparative analysis of existing MeRIP-seq data sets is presented to illustrate the advantage of using MVTs. RESULTS: Mean vector testing procedures are observed to control type I error rate and achieve high power for detecting differential RNA methylation using m6A-seq data. Results from two data sets indicate that the genes detected identified as having different m6A methylation patterns have high functional relevance to the study conditions. AVAILABILITY: The dimer software package for differential RNA methylation analysis is freely available at https://github.com/ouyang-lab/DIMER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

MODEL-BASED DISTANCE EMBEDDING WITH APPLICATIONS TO CHROMOSOMAL CONFORMATION BIOLOGY.

Recent development of high-throughput biotechnologies, such as Hi-C, have enabled genome-wide measurement of chromosomal conformation. The interaction signals among genomic loci are contaminated with noises. It remains largely unknown how well the underlying chromosomal conformation can be elucidated, based on massive and noisy measurements. We propose a new model-based distance embedding (MDE) framework, to reveal spatial organizations of chromosomes. The proposed framework is a general methodology, which allows us to link accurate probabilistic models, which characterize biological data properties, to efficiently recovering Euclidean distance matrices from noisy observations. The performance of MDE is shown through numerical experiments inspired by regular helix structure and random movement of chromosomes. The practical merits of MDE are also demonstrated by applications to real Hi-C data from both human and mouse cells which are further validated by gold standard benchmarks.

LISA2: Learning Complex Single-Cell Trajectory and Expression Trends.

Single-cell transcriptional and epigenomics profiles have been applied in a variety of tissues and diseases for discovering new cell types, differentiation trajectories, and gene regulatory networks. Many methods such as Monocle 2/3, URD, and STREAM have been developed for tree-based trajectory building. Here, we propose a fast and flexible trajectory learning method, LISA2, for single-cell data analysis. This new method has two distinctive features: (1) LISA2 utilizes specified leaves and root to reduce the complexity for building the developmental trajectory, especially for some special cases such as rare cell populations and adjacent terminal cell states; and (2) LISA2 is applicable for both transcriptomics and epigenomics data. LISA2 visualizes complex trajectories using 3D Landmark ISOmetric feature MAPping (L-ISOMAP). We apply LISA2 to simulation and real datasets in cerebellum, diencephalon, and hematopoietic stem cells including both single-cell transcriptomics data and single-cell assay for transposase-accessible chromatin data. LISA2 is efficient in estimating single-cell trajectory and expression trends for different kinds of molecular state of cells.

Multimodal single-cell analysis of cutaneous T-cell lymphoma reveals distinct subclonal tissue-dependent signatures.

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous group of mature T-cell neoplasms characterized by the accumulation of clonal malignant CD4+ T cells in the skin. The most common variant of CTCL, mycosis fungoides (MF ), is confined to the skin in early stages but can be accompanied by extracutaneous dissemination of malignant T cells to the blood and lymph nodes in advanced stages of disease. Sézary syndrome (SS), a leukemic form of disease, is characterized by significant blood involvement. Little is known about the transcriptional and genomic relationship between skin- and blood-residing malignant T cells in CTCL. To identify and interrogate malignant clones in matched skin and blood from patients with leukemic MF and SS, we combine T-cell receptor clonotyping with quantification of gene expression and cell surface markers at the single cell level. Our data reveal clonal evolution at a transcriptional and genetic level within the malignant populations of individual patients. We highlight highly consistent transcriptional signatures delineating skin- and blood-derived malignant T cells. Analysis of these 2 populations suggests that environmental cues, along with genetic aberrations, contribute to transcriptional profiles of malignant T cells. Our findings indicate that the skin microenvironment in CTCL promotes a transcriptional response supporting rapid malignant expansion, as opposed to the quiescent state observed in the blood, potentially influencing efficacy of therapies. These results provide insight into tissue-specific characteristics of cancerous cells and underscore the need to address the patients' individual malignant profiles at the time of therapy to eliminate all subclones.

Targeting eIF4A-Dependent Translation of KRAS Signaling Molecules.

Pancreatic adenocarcinoma (PDAC) epitomizes a deadly cancer driven by abnormal KRAS signaling. Here, we show that the eIF4A RNA helicase is required for translation of key KRAS signaling molecules and that pharmacological inhibition of eIF4A has single-agent activity against murine and human PDAC models at safe dose levels. EIF4A was uniquely required for the translation of mRNAs with long and highly structured 5' untranslated regions, including those with multiple G-quadruplex elements. Computational analyses identified these features in mRNAs encoding KRAS and key downstream molecules. Transcriptome-scale ribosome footprinting accurately identified eIF4A-dependent mRNAs in PDAC, including critical KRAS signaling molecules such as PI3K, RALA, RAC2, MET, MYC, and YAP1. These findings contrast with a recent study that relied on an older method, polysome fractionation, and implicated redox-related genes as eIF4A clients. Together, our findings highlight the power of ribosome footprinting in conjunction with deep RNA sequencing in accurately decoding translational control mechanisms and define the therapeutic mechanism of eIF4A inhibitors in PDAC. SIGNIFICANCE: These findings document the coordinate, eIF4A-dependent translation of RAS-related oncogenic signaling molecules and demonstrate therapeutic efficacy of eIF4A blockade in pancreatic adenocarcinoma.

A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets.

Traditional bulk RNA-sequencing of human pancreatic islets mainly reflects transcriptional response of major cell types. Single-cell RNA sequencing technology enables transcriptional characterization of individual cells, and thus makes it possible to detect cell types and subtypes. To tackle the heterogeneity of single-cell RNA-seq data, powerful and appropriate clustering is required to facilitate the discovery of cell types. In this paper, we propose a new clustering framework based on a graph-based model with various types of dissimilarity measures. We take the compositional nature of single-cell RNA-seq data into account and employ log-ratio transformations. The practical merit of the proposed method is demonstrated through the application to the centered log-ratio-transformed single-cell RNA-seq data for human pancreatic islets. The practical merit is also demonstrated through comparisons with existing single-cell clustering methods. The R-package for the proposed method can be found at https://github.com/Zhang-Data-Science-Research-Lab/LrSClust.

Direct DNA crosslinking with CAP-C uncovers transcription-dependent chromatin organization at high resolution.

Determining the spatial organization of chromatin in cells mainly relies on crosslinking-based chromosome conformation capture techniques, but resolution and signal-to-noise ratio of these approaches is limited by interference from DNA-bound proteins. Here we introduce chemical-crosslinking assisted proximity capture (CAP-C), a method that uses multifunctional chemical crosslinkers with defined sizes to capture chromatin contacts. CAP-C generates chromatin contact maps at subkilobase (sub-kb) resolution with low background noise. We applied CAP-C to formaldehyde prefixed mouse embryonic stem cells (mESCs) and investigated loop domains (median size of 200 kb) and nonloop domains (median size of 9 kb). Transcription inhibition caused a greater loss of contacts in nonloop domains than loop domains. We uncovered conserved, transcription-state-dependent chromatin compartmentalization at high resolution that is shared from Drosophila to human, and a transcription-initiation-dependent nuclear subcompartment that brings multiple nonloop domains in close proximity. We also showed that CAP-C could be used to detect native chromatin conformation without formaldehyde prefixing.

Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data.

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.

Meta-analysis of peptides to detect protein significance.

Shotgun assays are widely used in biotechnologies to characterize large molecules, which are hard to be measured as a whole directly. For instance, in Liquid Chromatography - Mass Spectrometry (LC-MS) shotgun experiments, proteins in biological samples are digested into peptides, and then peptides are separated and measured. However, in proteomics study, investigators are usually interested in the performance of the whole proteins instead of those peptide fragments. In light of meta-analysis, we propose an adaptive thresholding method to select informative peptides, and combine peptide-level models to protein-level analysis. The meta-analysis procedure and modeling rationale can be adapted to data analysis of other types of shotgun assays.

Large-scale analysis of the position-dependent binding and regulation of human RNA binding proteins.

BACKGROUND: RNA binding proteins (RBPs) play essential roles in the regulation of RNA metabolism. Recent studies have disclosed that RBPs achieve their functions via binding to their targets in a position-dependent pattern on RNAs. However, few studies have systematically addressed the associations between the RBP's functions and their positional binding preferences. METHODS: Here, we present large-scale analyses on the functional targets of human RBPs by integrating the enhanced cross-linking and immunoprecipitation followed by sequencing (eCLIP-seq) datasets and the shRNA knockdown followed by RNA-seq datasets that are deposited in the integrated ENCyclopedia of DNA Elements in the human genome (ENCODE) data portal. RESULTS: We found that (1) binding to the translation termination site and the 3'untranslated region is important to most human RBPs in the RNA decay regulation; (2) RBPs' binding and regulation follow a cell-type specific pattern. CONCLUSIONS: These analysis results show the strong relationship between the binding position and the functions of RBPs, which provides novel insights into the RBPs' regulation mechanisms.

PATH: An interactive web platform for analysis of time-course high-dimensional genomic data.

Discovering patterns in time-course genomic data can provide insights on the dynamics of biological systems in health and disease. Here, we present a Platform for Analysis of Time-course High-dimensional data (PATH) with applications in genomics research. This web application provides a user-friendly interface with interactive data visualisation, dimension reduction, pattern discovery, and feature selection based on the principal trend analysis (PTA). Furthermore, the web application enables interactive and integrative analysis of time-course high-dimensional data based on the Joint PTA. The utilities of PATH are demonstrated through simulated and real examples, and the comparison with classical time-course data analysis methods such as the functional principal component analysis. PATH is freely accessible at https://ouyanglab.shinyapps.io/PATH/.

Identification and analysis of RNA structural disruptions induced by single nucleotide variants using Riprap and RiboSNitchDB.

RNA conformational alteration has significant impacts on cellular processes and phenotypic variations. An emerging genetic factor of RNA conformational alteration is a new class of single nucleotide variant (SNV) named riboSNitch. RiboSNitches have been demonstrated to be involved in many genetic diseases. However, identifying riboSNitches is notably difficult as the signals of RNA structural disruption are often subtle. Here, we introduce a novel computational framework-RIboSNitch Predictor based on Robust Analysis of Pairing probabilities (Riprap). Riprap identifies structurally disrupted regions around any given SNVs based on robust analysis of local structural configurations between wild-type and mutant RNA sequences. Compared to previous approaches, Riprap shows higher accuracy when assessed on hundreds of known riboSNitches captured by various experimental RNA structure probing methods including the parallel analysis of RNA structure (PARS) and the selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE). Further, Riprap detects the experimentally validated riboSNitch that regulates human catechol-O-methyltransferase haplotypes and outputs structurally disrupted regions precisely at base resolution. Riprap provides a new approach to interpreting disease-related genetic variants. In addition, we construct a database (RiboSNitchDB) that includes the annotation and visualization of all presented riboSNitches in this study as well as 24 629 predicted riboSNitches from human expression quantitative trait loci.

PRAS: Predicting functional targets of RNA binding proteins based on CLIP-seq peaks.

RNA-protein interaction plays important roles in post-transcriptional regulation. Recent advancements in cross-linking and immunoprecipitation followed by sequencing (CLIP-seq) technologies make it possible to detect the binding peaks of a given RNA binding protein (RBP) at transcriptome scale. However, it is still challenging to predict the functional consequences of RBP binding peaks. In this study, we propose the Protein-RNA Association Strength (PRAS), which integrates the intensities and positions of the binding peaks of RBPs for functional mRNA targets prediction. We illustrate the superiority of PRAS over existing approaches on predicting the functional targets of two related but divergent CELF (CUGBP, ELAV-like factor) RBPs in mouse brain and muscle. We also demonstrate the potential of PRAS for wide adoption by applying it to the enhanced CLIP-seq (eCLIP) datasets of 37 RNA decay related RBPs in two human cell lines. PRAS can be utilized to investigate any RBPs with available CLIP-seq peaks. PRAS is freely available at http://ouyanglab.jax.org/pras/.