Surfaceome interrogation using an RNA-seq approach highlights leukemia initiating cell biomarkers in an LMO2 T cell transgenic model.

The surfaceome is critical because surface proteins provide a gateway for internal signals and transfer of molecules into cells, and surfaceome differences can influence therapy response. We have used a surfaceome analysis method, based on comparing RNA-seq data between normal and abnormal cells (Surfaceome DataBase Mining or Surfaceome DBM), to identify sets of upregulated cell surface protein mRNAs in an LMO2-mediated T-ALL mouse model and corroborated by protein detection using antibodies. In this model the leukemia initiating cells (LICs) comprise pre-leukaemic, differentiation inhibited thymocytes allowing us to provide a profile of the LIC surfaceome in which GPR56, CD53 and CD59a are co-expressed with CD25. Implementation of cell surface interaction assays demonstrates fluid interaction of surface proteins and CD25 is only internalized when co-localized with other proteins. The Surfaceome DBM approach to analyse cancer cell surfaceomes is a way to find targetable surface biomarkers for clinical conditions where RNA-seq data from normal and abnormal cell are available.

Exploring the surfaceome of Ewing sarcoma identifies a new and unique therapeutic target.

The cell surface proteome of tumors mediates the interface between the transformed cells and the general microenvironment, including interactions with stromal cells in the tumor niche and immune cells such as T cells. In addition, the cell surface proteome of individual cancers defines biomarkers for that tumor type and potential proteins that can be the target of antibody-mediated therapy. We have used next-generation deep RNA sequencing (RNA-seq) coupled to an in-house database of genes encoding cell surface proteins (herein referred to as the surfaceome) as a tool to define a cell surface proteome of Ewing sarcoma compared with progenitor mesenchymal stem cells. This subtractive RNA-seq analysis revealed a specific surfaceome of Ewing and showed unexpectedly that the leucine-rich repeat and Ig domain protein 1 (LINGO1) is expressed in over 90% of Ewing sarcoma tumors, but not expressed in any other somatic tissue apart from the brain. We found that the LINGO1 protein acts as a gateway protein internalizing into the tumor cells when engaged by antibody and can carry antibody conjugated with drugs to kill Ewing sarcoma cells. Therefore, LINGO1 is a new, unique, and specific biomarker and drug target for the treatment of Ewing sarcoma.

Reducing ligation bias of small RNAs in libraries for next generation sequencing.

BACKGROUND: The use of nucleic acid-modifying enzymes has driven the rapid advancement in molecular biology. Understanding their function is important for modifying or improving their activity. However, functional analysis usually relies upon low-throughput experiments. Here we present a method for functional analysis of nucleic acid-modifying enzymes using next generation sequencing. FINDINGS: We demonstrate that sequencing data of libraries generated by RNA ligases can reveal novel secondary structure preferences of these enzymes, which are used in small RNA cloning and library preparation for NGS. Using this knowledge we demonstrate that the cloning bias in small RNA libraries is RNA ligase-dependent. We developed a high definition (HD) protocol that reduces the RNA ligase-dependent cloning bias. The HD protocol doubled read coverage, is quantitative and found previously unidentified microRNAs. In addition, we show that microRNAs in miRBase are those preferred by the adapters of the main sequencing platform. CONCLUSIONS: Sequencing bias of small RNAs partially influenced which microRNAs have been studied in depth; therefore most previous small RNA profiling experiments should be re-evaluated. New microRNAs are likely to be found, which were selected against by existing adapters. Preference of currently used adapters towards known microRNAs suggests that the annotation of all existing small RNAs, including miRNAs, siRNAs and piRNAs, has been biased.

Regulation of multiple target genes by miR-1 and miR-206 is pivotal for C2C12 myoblast differentiation.

MicroRNAs are short non-coding RNAs involved in post-transcriptional regulation of multiple messenger RNA targets. The miR-1/miR-206 family is expressed during skeletal muscle differentiation and is an integral component of myogenesis. To better understand miR-1/miR-206 function during myoblast differentiation we identified novel target mRNAs by microarray and characterized their function in C2C12 myoblasts. Candidate targets from the screen were experimentally validated together with target genes that were predicted by three different algorithms. Some targets characterised have a known function in skeletal muscle development and/or differentiation and include Meox2, RARB, Fzd7, MAP4K3, CLCN3 and NFAT5, others are potentially novel regulators of myogenesis, such as the chromatin remodelling factors Smarcd2 and Smarcb1 or the anti-apoptotic protein SH3BGRL3. The expression profiles of confirmed target genes were examined during C2C12 cell myogenesis. We found that inhibition of endogenous miR-1 and miR-206 by antimiRs blocked the downregulation of most targets in differentiating cells, thus indicating that microRNA activity and target interaction is required for muscle differentiation. Finally, we show that sustained expression of validated miR-1 and/or miR-206 targets resulted in increased proliferation and inhibition of C2C12 cell myogenesis. In many cases the expression of genes related to non-muscle cell fates, such as chondrogenesis, was activated. This indicates that the concerted downregulation of multiple microRNA targets is not only crucial to the skeletal muscle differentiation program but also serves to prevent alternative cell fate choices.

Small RNA discovery and characterisation in eukaryotes using high-throughput approaches.

RNA silencing is a mechanism of genetic regulation that is mediated by short noncoding RNAs, or small RNAs (sRNAs). Regulatory interactions are established based on nucleotide sequence complementarity between the sRNAs and their targets. The development of new high-throughput sequencing technologies has accelerated the discovery of sRNAs in a variety of plants and animals. The use of these and other high-throughput technologies, such as microarrays, to measure RNA and protein concentrations of gene products potentially regulated by sRNAs has also been important for their functional characterisation. mRNAs targeted by sRNAs can produce new sRNAs or the protein encoded by the target mRNA can regulate other mRNAs. In either case the targeting sRNAs are parts of complex RNA networks therefore identifying and characterising sRNAs contribute to better understanding of RNA networks. In this chapter we will review RNA silencing, the different types of sRNAs that mediate it and the computational methods that have been developed to use high-throughput technologies in the study of sRNAs and their targets.

mRNA expression profiling reveals conserved and non-conserved miR-140 targets.

microRNAs are non-coding RNAs that regulate gene expression. A significant proportion of microRNAs is perfectly conserved across the vertebrate clade, including miR-140, which is specifically expressed in cartilage. Although it has been computationally predicted that a large majority of microRNA targets are conserved, experimental evidence for this hypothesis remains scarce. In this work we use mRNA expression profiles obtained after manipulation of miR-140 activity levels in human and chicken primary chondrocytes to explore the extent of miR-140 target conservation. Our data suggest that miR-140 has a large number of targets conserved between human and chicken and we validate one of these, BMP2. However, we also found a significant number of non-conserved targets in the two species. In addition, we found that a commercially available scrambled siRNA, which is regularly used as a negative control, regulate the accumulation of many genes.

Analyzing mRNA expression identifies Smad3 as a microRNA-140 target regulated only at protein level.

mRNA profiling is routinely used to identify microRNA targets, however, this high-throughput technology is not suitable for identifying targets regulated only at protein level. Here, we have developed and validated a novel methodology based on computational analysis of promoter sequences combined with mRNA microarray experiments to reveal transcription factors that are direct microRNA targets at the protein level. Using this approach we identified Smad3, a key transcription factor in the TGFbeta signaling pathway, as a direct miR-140 target. We showed that miR-140 suppressed the TGFbeta pathway through repression of Smad3 and that TGFbeta suppressed the accumulation of miR-140 forming a double negative feedback loop. Our findings establish a valid strategy for the discovery of microRNA targets regulated only at protein level, and we propose that additional targets could be identified by re-analysis of existing microarray datasets.

High throughput sequencing of microRNAs in chicken somites.

High throughput Solexa sequencing technology was applied to identify microRNAs in somites of developing chicken embryos. We obtained 651,273 reads, from which 340,415 were mapped to the chicken genome representing 1701 distinct sequences. Eighty-five of these were known microRNAs and 42 novel miRNA candidates were identified. Accumulation of 18 of 42 sequences was confirmed by Northern blot analysis. Ten of the 18 sequences are new variants of known miRNAs and eight short RNAs are novel miRNAs. Six of these eight have not been reported by other deep sequencing projects. One of the six new miRNAs is highly enriched in somite tissue suggesting that deep sequencing of other specific tissues has the potential to identify novel tissue specific miRNAs.

Experimental identification of microRNA-140 targets by silencing and overexpressing miR-140.

MicroRNAs (miRNAs) are short noncoding RNA molecules regulating the expression of mRNAs. Target identification of miRNAs is computationally difficult due to the relatively low homology between miRNAs and their targets. We present here an experimental approach to target identification where the cartilage-specific miR-140 was overexpressed and silenced in cells it is normally expressed in separate experiments. Expression of mRNAs was profiled in both experiments and the intersection of mRNAs repressed by miR-140 overexpression and derepressed by silencing of miR-140 was identified. The intersection contained only 49 genes, although both treatments affected the accumulation of hundreds of mRNAs. These 49 genes showed a very strong enrichment for the miR-140 seed sequence implying that the approach is efficient and specific. Twenty-one of these 49 genes were predicted to be direct targets based on the presence of the seed sequence. Interestingly, none of these were predicted by the published target prediction methods we used. One of the potential target mRNAs, Cxcl12, was experimentally validated by Northern blot analysis and a luciferase reporter assay.

YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae.

The Yeast search for transcriptional regulators and consensus tracking (YEASTRACT) information system (www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in September 2007, this database contains over 30 990 regulatory associations between Transcription Factors (TFs) and target genes and includes 284 specific DNA binding sites for 108 characterized TFs. Computational tools are also provided to facilitate the exploitation of the gathered data when solving a number of biological questions, in particular the ones that involve the analysis of global gene expression results. In this new release, YEASTRACT includes DISCOVERER, a set of computational tools that can be used to identify complex motifs over-represented in the promoter regions of co-regulated genes. The motifs identified are then clustered in families, represented by a position weight matrix and are automatically compared with the known transcription factor binding sites described in YEASTRACT. Additionally, in this new release, it is possible to generate graphic depictions of transcriptional regulatory networks for documented or potential regulatory associations between TFs and target genes. The visual display of these networks of interactions is instrumental in functional studies. Tutorials are available on the system to exemplify the use of all the available tools.