Non-coding RNA (ncRNA) transcripts are RNA molecules that do not code for proteins, but elicit function by other mechanisms. The vast majority of RNA produced in a cell is non-coding ribosomal RNA, produced from relatively few loci, however more recently complementary DNA (cDNA) cloning, tag sequencing, and genome tiling array studies suggest that ncRNAs also account for the majority of RNA species produced by a cell. ncRNA based regulation has been referred to as a ‘hidden layer’ of signals or ‘dark matter’ that control gene expression in cellular processes by poorly described mechanisms. These terms have appeared as ncRNAs until recently have been ignored by expression profiling and cDNA annotation projects and their mode of action is diverse (e.g. influencing chromatin structure and epigenetics, translational silencing, transcriptional silencing). Here, we highlight recent functional genomics strategies toward identifying and assigning function to ncRNA transcription.

The international Functional Annotation Of the Mammalian Genomes 4 (FANTOM4) research collaboration set out to better understand the transcriptional network that regulates macrophage differentiation and to uncover novel components of the transcriptome employing a series of high-throughput experiments. The primary and unique technique is cap analysis of gene expression (CAGE), sequencing mRNA 5′-ends with a second-generation sequencer to quantify promoter activities even in the absence of gene annotation. Additional genome-wide experiments complement the setup including short RNA sequencing, microarray gene expression profiling on large-scale perturbation experiments and ChIP–chip for epigenetic marks and transcription factors. All the experiments are performed in a differentiation time course of the THP-1 human leukemic cell line. Furthermore, we performed a large-scale mammalian two-hybrid (M2H) assay between transcription factors and monitored their expression profile across human and mouse tissues with qRT-PCR to address combinatorial effects of regulation by transcription factors. These interdependent data have been analyzed individually and in combination with each other and are published in related but distinct papers. We provide all data together with systematic annotation in an integrated view as resource for the scientific community (http://fantom.gsc.riken.jp/4/). Additionally, we assembled a rich set of derived analysis results including published predicted and validated regulatory interactions. Here we introduce the resource and its update after the initial release.

Perturbation and time-course data sets, in combination with computational approaches, can be used to infer transcriptional regulatory networks which ultimately govern the developmental pathways and responses of cells. Here, we individually knocked down the four transcription factors PU.1, IRF8, MYB and SP1 in the human monocyte leukemia THP-1 cell line and profiled the genome-wide transcriptional response of individual transcription starting sites using deep sequencing based Cap Analysis of Gene Expression. From the proximal promoter regions of the responding transcription starting sites, we derived de novo binding-site motifs, characterized their biological function and constructed a network. We found a previously described composite motif for PU.1 and IRF8 that explains the overlapping set of transcriptional responses upon knockdown of either factor.

Background

MicroRNAs (miRNAs) are short single stranded noncoding RNAs that suppress gene expression through either translational repression or degradation of target mRNAs. The annealing between messenger RNAs and 5′ seed region of miRNAs is believed to be essential for the specific suppression of target gene expression. One miRNA can have several hundred different targets in a cell. Rapidly accumulating evidence suggests that many miRNAs are involved in cell cycle regulation and consequentially play critical roles in carcinogenesis.

Methodology/Principal Findings

Introduction of synthetic miR-107 or miR-185 suppressed growth of the human non-small cell lung cancer cell lines. Flow cytometry analysis revealed these miRNAs induce a G1 cell cycle arrest in H1299 cells and the suppression of cell cycle progression is stronger than that by Let-7 miRNA. By the gene expression analyses with oligonucleotide microarrays, we find hundreds of genes are affected by transfection of these miRNAs. Using miRNA-target prediction analyses and the array data, we listed up a set of likely targets of miR-107 and miR-185 for G1 cell cycle arrest and validate a subset of them using real-time RT-PCR and immunoblotting for CDK6.

Conclusions/Significance

We identified new cell cycle regulating miRNAs, miR-107 and miR-185, localized in frequently altered chromosomal regions in human lung cancers. Especially for miR-107, a large number of down-regulated genes are annotated with the gene ontology term ‘cell cycle’. Our results suggest that these miRNAs may contribute to regulate cell cycle in human malignant tumors.

Background

Cap analysis of gene expression (CAGE) is a 5′ sequence tag technology to globally determine transcriptional starting sites in the genome and their expression levels and has most recently been adapted to the HeliScope single molecule sequencer. Despite significant simplifications in the CAGE protocol, it has until now been a labour intensive protocol.

Methodology

In this study we set out to adapt the protocol to a robotic workflow, which would increase throughput and reduce handling. The automated CAGE cDNA preparation system we present here can prepare 96 ‘HeliScope ready’ CAGE cDNA libraries in 8 days, as opposed to 6 weeks by a manual operator.We compare the results obtained using the same RNA in manual libraries and across multiple automation batches to assess reproducibility.

Conclusions

We show that the sequencing was highly reproducible and comparable to manual libraries with an 8 fold increase in productivity. The automated CAGE cDNA preparation system can prepare 96 CAGE sequencing samples simultaneously. Finally we discuss how the system could be used for CAGE on Illumina/SOLiD platforms, RNA-seq and full-length cDNA generation.

Background

Mesothelioma is a highly malignant tumor that is primarily caused by occupational or environmental exposure to asbestos fibers. Despite worldwide restrictions on asbestos usage, further cases are expected as diagnosis is typically 20–40 years after exposure. Once diagnosed there is a very poor prognosis with a median survival rate of 9 months. Considering this the development of early pre clinical diagnostic markers may help improve clinical outcomes.

Methodology

Microarray expression arrays on mesothelium and other tissues dissected from mice were used to identify candidate mesothelial lineage markers. Candidates were further tested by qRTPCR and in-situ hybridization across a mouse tissue panel. Two candidate biomarkers with the potential for secretion, uroplakin 3B (UPK3B), and leucine rich repeat neuronal 4 (LRRN4) and one commercialized mesothelioma marker, mesothelin (MSLN) were then chosen for validation across a panel of normal human primary cells, 16 established mesothelioma cell lines, 10 lung cancer lines, and a further set of 8 unrelated cancer cell lines.

Conclusions

Within the primary cell panel, LRRN4 was only detected in primary mesothelial cells, but MSLN and UPK3B were also detected in other cell types. MSLN was detected in bronchial epithelial cells and alveolar epithelial cells and UPK3B was detected in retinal pigment epithelial cells and urothelial cells. Testing the cell line panel, MSLN was detected in 15 of the 16 mesothelioma cells lines, whereas LRRN4 was only detected in 8 and UPK3B in 6. Interestingly MSLN levels appear to be upregulated in the mesothelioma lines compared to the primary mesothelial cells, while LRRN4 and UPK3B, are either lost or down-regulated. Despite the higher fraction of mesothelioma lines positive for MSLN, it was also detected at high levels in 2 lung cancer lines and 3 other unrelated cancer lines derived from papillotubular adenocarcinoma, signet ring carcinoma and transitional cell carcinoma.

The international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM2, comprised 60,770 full-length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein-coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full-length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web-based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full-length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding (including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full-length cDNAs. The total number of distinct non-protein-coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and final expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.