The pervasive transcription of our genome presents a possibility of revealing new genomic functions by investigating RNA interactions. Current methods for mapping RNA–RNA interactions have to rely on an ‘anchor' protein or RNA and often require molecular perturbations. Here we present the MARIO (Mapping RNA interactome in vivo) technology to massively reveal RNA–RNA interactions from unperturbed cells. We mapped tens of thousands of endogenous RNA–RNA interactions from mouse embryonic stem cells and brain. We validated seven interactions by RNA antisense purification and one interaction using single-molecule RNA–FISH. The experimentally derived RNA interactome is a scale-free network, which is not expected from currently perceived promiscuity in RNA–RNA interactions. Base pairing is observed at the interacting regions between long RNAs, including transposon transcripts, suggesting a class of regulatory sequences acting in trans. In addition, MARIO data reveal thousands of intra-molecule interactions, providing in vivo data on high-order RNA structures.

We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer. GemiNI integrates not only gene and miRNA expression data but also computationally derived information about TF–target gene and miRNA–mRNA interactions. Literature validation shows that the integrated modeling of expression data and FFLs better identifies cancer-related TFs and miRNAs compared to existing approaches. We have utilized GemiNI for analyzing six data sets of solid cancers (liver, kidney, prostate, lung and germ cell) and found that top-ranked FFLs account for ∼20% of transcriptome changes between normal and cancer. We have identified common FFL regulators across multiple cancer types, such as known FFLs consisting of MYC and miR-15/miR-17 families, and novel FFLs consisting of ARNT, CREB1 and their miRNA partners. The results and analysis web server are available at http://www.canevolve.org/dChip-GemiNi.

Fusion transcripts are used as biomarkers in companion diagnoses. Although more than 15,000 fusion RNAs have been identified from diverse cancer types, few common features have been reported. Here, we compared 16,410 fusion transcripts detected in cancer (from a published cohort of 9,966 tumor samples of 33 cancer types) with genome-wide RNA–DNA interactions mapped in two normal, noncancerous cell types [using iMARGI, an enhanced version of the mapping of RNA–genome interactions (MARGI) assay]. Among the top 10 most significant RNA–DNA interactions in normal cells, 5 colocalized with the gene pairs that formed fusion RNAs in cancer. Furthermore, throughout the genome, the frequency of a gene pair to exhibit RNA–DNA interactions is positively correlated with the probability of this gene pair to present documented fusion transcripts in cancer. To test whether RNA–DNA interactions in normal cells are predictive of fusion RNAs, we analyzed these in a validation cohort of 96 lung cancer samples using RNA sequencing (RNA-seq). Thirty-seven of 42 fusion transcripts in the validation cohort were found to exhibit RNA–DNA interactions in normal cells. Finally, by combining RNA-seq, single-molecule RNA FISH, and DNA FISH, we detected a cancer sample with EML4-ALK fusion RNA without forming the EML4-ALK fusion gene. Collectively, these data suggest an RNA-poise model, where spatial proximity of RNA and DNA could poise for the creation of fusion transcripts.

Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data. Surprisingly, there has been no serious attempt to review the currently available methodologies or the novel insights brought using them. In this work, we discuss the quantitative relationships observed between CNA and gene expression in multiple cancer types and biological milestones achieved using the available methodologies. We discuss the conceptual evolution of both, the step-wise and the joint data integration methodologies over the last decade. We conclude by providing suggestions for building efficient data integration methodologies and asking further biological questions.

Nanobodies (Nbs) or single-domain antibodies are among the smallest and most stable binder scaffolds known. In vitro display is a powerful antibody discovery technique used worldwide. We describe the first adaptation of in vitro mRNA/cDNA display for the rapid, automatable discovery of Nbs against desired targets, and use it to discover the first ever reported nanobody against the human full-length glucose transporter, GLUT-1. We envision our streamlined method as a bench-top platform technology, in combination with various molecular evolution techniques, for expedited Nb discovery.

SummaryIt remains challenging to identify all parts of the nuclear genome that are in proximity to nuclear speckles, due to physical separation between the nuclear speckle cores and chromatin. We hypothesized that noncoding RNAs including small nuclear RNA (snRNAs) and Malat1, which accumulate at the periphery of nuclear speckles (nsaRNA [nuclear speckle-associated RNA]), may extend to sufficient proximity to the genome. Leveraging a transcriptome-genome interaction assay (mapping of RNA-genome interactions [MARGI]), we identified clusters of nsaRNA-interacting genomic sequences (nsaPeaks). Posttranscriptional pre-mRNAs, which also accumulate to nuclear speckles, exhibited proximity to nsaPeaks but rarely to other genomic regions. Our combined DNA fluorescence in situ hybridization and immunofluorescence analysis in 182 single cells revealed a 3-fold increase in odds for nuclear speckles to localize near an nsaPeak than its neighboring genomic sequence. These data suggest a model that nsaRNAs are located in sufficient proximity to the nuclear genome and leave identifiable genomic footprints, thus revealing the parts of genome proximal to nuclear speckles.

Background Compared to proteins, glycans, and lipids, much less is known about RNAs on the cell surface. We develop a series of technologies to test for any nuclear-encoded RNAs that are stably attached to the cell surface and exposed to the extracellular space, hereafter called membrane-associated extracellular RNAs (maxRNAs). Results We develop a technique called Surface-seq to selectively sequence maxRNAs and validate two Surface-seq identified maxRNAs by RNA fluorescence in situ hybridization. To test for cell-type specificity of maxRNA, we use antisense oligos to hybridize to single-stranded transcripts exposed on the surface of human peripheral blood mononuclear cells (PBMCs). Combining this strategy with imaging flow cytometry, single-cell RNA sequencing, and maxRNA sequencing, we identify monocytes as the major type of maxRNA+ PBMCs and prioritize 11 candidate maxRNAs for functional tests. Extracellular application of antisense oligos of FNDC3B and CTSS transcripts inhibits monocyte adhesion to vascular endothelial cells. Conclusions Collectively, these data highlight maxRNAs as functional components of the cell surface, suggesting an expanded role for RNA in cell-cell and cell-environment interactions.

Fusion transcripts are used as biomarkers in companion diagnoses. Although more than 15,000 fusion RNAs have been identified from diverse cancer types, few common features have been reported. Here, we compared 16,410 fusion transcripts detected in cancer (from a published cohort of 9,966 tumor samples of 33 cancer types) with genome-wide RNA-DNA interactions mapped in two normal, non-cancerous cell types (using iMARGI, an enhanced version of the MARGI [Mapping RNA-Genome Interactions assay]). Among the top 10 most significant RNA-DNA interactions in normal cells, 5 co-localized with the gene pairs that formed fusion RNAs in cancer. Furthermore, throughout the genome, the frequency of a gene pair to exhibit RNA-DNA interactions is positively correlated with the probability of this gene pair to present documented fusion transcripts in cancer. To test whether RNA-DNA interactions in normal cells are predictive of fusion RNAs, we analyzed these in a validation cohort of 96 lung cancer samples using RNA-seq. 37 out of 42 fusion transcripts in the validation cohort were found to exhibit RNA-DNA interactions in normal cells. Finally, by combining RNA-seq, single-molecule RNA FISH, and DNA FISH, we detected a cancer sample with EML4-ALK fusion RNA without forming the EML4-ALK fusion gene. Collectively, these data suggest a novel RNA-poise model, where spatial proximity of RNA and DNA could poise for the creation of fusion transcripts. 1 2 3 4 5 6 7 8 9 10 11 fusion transcript | RNA-DNA interaction | RNA-poise model F usion transcripts are associated with diverse cancer types and have been proposed as diagnostic biomarkers (1-3). 11Companion tests and targeted therapies have been developed to identify and treat fusion-gene defined cancer subtypes 12 (2, 4). Efforts of detection of fusion transcripts have primarily relied on analyses of RNA sequencing (RNA-seq) data (1, 3,(5)(6)(7)(8). 13 A recent study analyzed 9,966 RNA-seq datasets across 33 cancer types from The Cancer Genome Atlas (TCGA) and identified 14 more than 15,000 fusion transcripts (4). 15 Despite the large number of gene pairs in identified fusion transcripts, it remains formidable to predict what unreported 16 pair of genes may form a new fusion transcript. Recent analyses could not identify any distinct feature of fusion RNA forming 17 gene pairs (9). Here, we report a characteristic pattern of the genomic locations of the gene pairs involved in fusion transcripts. 18 Chromatin-associated RNAs (caRNAs) provide an additional layer of epigenomic information in parallel to DNA and histone 19 modifications (10). The recently developed MARGI (Mapping RNA-Genome Interactions) technology enabled identification of 20 diverse caRNAs and the respective genomic interacting locations of each caRNA (6). In this work, we used an improved MARGI 21 experimental pipeline to map RNA-DNA interactions in human embryonic kidney (HEK) and human foreskin fibroblast (HFF) 22 cells. The detected RNA-DNA interactions often appeared on the gene pairs involved in cancer-derived fus...