Non-coding RNA Landscape Research Articles

Spatial-temporal transcriptional dynamics of long non-coding RNAs in human brain.

The functional architecture of the human brain is greatly determined by the temporal and spatial regulation of the transcription process. However, the spatial and temporal transcriptional landscape of long non-coding RNAs (lncRNAs) during human brain development remains poorly understood. Here, we report the genome-wide lncRNA transcriptional analysis in an extensive series of 1340 post-mortem human brain specimens collected from 16 regions spanning the period from early embryo development to late adulthood. We discovered that lncRNA transcriptome dramatically changed during fetal development, while transited to a surprisingly relatively stable state after birth till the late adulthood. We also discovered that the transcription map of lncRNAs was spatially different, and that this spatial difference was developmentally regulated. Of the 16 brain regions explored (cerebellar cortex, thalamus, striatum, amygdala, hippocampus and 11 neocortex areas), cerebellar cortex showed the most distinct lncRNA expression features from all remaining brain regions throughout the whole developmental period, reflecting its unique developmental and functional features. Furthermore, by characterizing the functional modules and cellular processes of the spatial-temporal dynamic lncRNAs, we found that they were significantly associated with the RNA processing, neuron differentiation and synaptic signal transportation processes. Furthermore, we found that many lncRNAs associated with the neurodegenerative Alzheimer and Parkinson diseases were co-expressed in the fetal development of the human brain, and affected the convergent biological processes. In summary, our study provides a comprehensive map for lncRNA transcription dynamics in human brain development, which might shed light on the understanding of the molecular underpinnings of human brain function and disease.

DecodeRNA- predicting non-coding RNA functions using guilt-by-association.

Although the long non-coding RNA (lncRNA) landscape is expanding rapidly, only a small number of lncRNAs have been functionally annotated. Here, we present decodeRNA (http://www.decoderna.org), a database providing functional contexts for both human lncRNAs and microRNAs in 29 cancer and 12 normal tissue types. With state-of-the-art data mining and visualization options, easy access to results and a straightforward user interface, decodeRNA aims to be a powerful tool for researchers in the ncRNA field. Database URL: http://www.decoderna.org

Navigating the dynamic landscape of long noncoding RNA and protein-coding gene annotations in GENCODE.

BackgroundOur understanding of the transcriptional potential of the genome and its functional consequences has undergone a significant change in the last decade. This has been largely contributed by the improvements in technology which could annotate and in many cases functionally characterize a number of novel gene loci in the human genome. Keeping pace with advancements in this dynamic environment and being able to systematically annotate a compendium of genes and transcripts is indeed a formidable task. Of the many databases which attempted to systematically annotate the genome, GENCODE has emerged as one of the largest and popular compendium for human genome annotations.ResultsThe analysis of various versions of GENCODE revealed that there was a constant upgradation of transcripts for both protein-coding and long noncoding RNA (lncRNAs) leading to conflicting annotations. The GENCODE version 24 accounts for 4.18 % of the human genome to be transcribed which is an increase of 1.58 % from its first version. Out of 2,51,614 transcripts annotated across GENCODE versions, only 21.7 % had consistency. We also examined GENCODE consortia categorized transcripts into 70 biotypes out of which only 17 remained stable throughout.ConclusionsIn this report, we try to review the impact on the dynamicity with respect to gene annotations, specifically (lncRNA) annotations in GENCODE over the years. Our analysis suggests a significant dynamism in gene annotations, reflective of the evolution and consensus in nomenclature of genes. While a progressive change in annotations and timely release of the updates make the resource reliable in the community, the dynamicity with each release poses unique challenges to its users. Taking cues from other experiments with bio-curation, we propose potential avenues and methods to mend the gap.Electronic supplementary materialThe online version of this article (doi:10.1186/s40246-016-0090-2) contains supplementary material, which is available to authorized users.

The Novel Landscape of Long Non-Coding RNAs in Response to Human Foamy Virus Infection Characterized by RNA-Seq.

Human foamy virus (HFV) is a complex and unique retrovirus with the longest genomes among retroviruses that are used as vectors for gene therapy. Long non-coding RNAs (lncRNAs) are regarded as key regulators that are involved in diverse biological processes during viral infection. However, the role of lncRNAs in HFV infection remains unknown. In this study, we utilized next-generation sequencing to first characterize lncRNAs in 293T cells after HFV infection, evaluating length distribution, exon number distribution, volcano picture, and lncRNA class distribution. We identified 11,336 lncRNAs (4,729 upregulated lncRNAs and 6,588 downregulated lncRNAs) and 61,367 mRNAs (30,133 upregulated mRNAs and 31,220 downregulated mRNAs), which were differentially expressed in the HFV-infected 293T cells. Subsequently, six differentially expressed lncRNAs characterized using RNA-seq were confirmed by quantitative real-time polymerase chain reaction assays. Interestingly, Gene Ontology (GO)/Gene Ontology Tree Machine (GOTM) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway analyses indicated that positive regulation of interleukin 8 (IL8) production and cytokine-cytokine receptor interaction might be involved in the functional enrichment of lncRNAs. Moreover, cis-acting and trans-acting regulatory networks show that NR_028036 may target the fas gene in a cis-acting manner and that ENST00000354838 may target the IL18 gene in a trans-acting manner. Overall, these results not only provide novel insights into the relationship between HFV and lncRNAs in the host response to infection but also have implications for the future wider application of HFV as a vector.

Abstract 2662: Interrogation of the landscape of long noncoding RNAs in breast cancer to identify an ER-regulated predictor of tamoxifen resistance

Abstract Background: We previously performed a bioinformatics-based analysis on 7,256 RNA sequencing libraries from tumors, normal tissues, and cell lines to delineate the landscape of long noncoding RNAs (lncRNAs) in the human transcriptome. This analysis identified 58,648 lncRNAs, including over 45,000 novel transcripts (Iyer and Niknafs et al, Nature Genetics, 2015). We now interrogate this lncRNA compendium to identify top candidate estrogen receptor (ER)-associated lncRNAs in breast cancer and characterize their association with disease progression. Methods: To identify and prioritize lncRNAs that were differentially expressed in cancer vs normal tissue, and in ER-positive vs ER-negative disease, we performed differential expression testing on over 1000 RNA sequencing libraries including breast cancer and normal tissue samples from the The Cancer Genome Atlas (TCGA) project. The effect of the top prioritized lncRNA on cancer phenotypes was studied via proliferation, colony formation, invasion and tamoxifen resistance assays in MCF7 and T47D cell lines, and via mouse xenograft studies. The mechanism by which this lncRNA promotes tumor progression was investigated by identifying its top protein interactors and its subdomains responsible for function, and then studying the effects of disrupting function of this lncRNA on cancer phenotypes. Results: Differential expression analyses nominated BRCAT431 as the top overexpressed ER-regulated lncRNA in breast cancer. In vitro experiments demonstrate that shRNA-mediated knockdown of BRCAT431 significantly decreased proliferation, colony formation, and invasion (by >50% in most assays). Tamoxifen resistance was associated with significantly increased BRCAT431 levels in both MCF7 and T47D cells, and knockdown of BRCAT431 reversed tamoxifen resistance. Xenograft studies demonstrate that knockdown of BRCAT431 also significantly decreased xenograft growth and tumor invasion by >50%. RNA pulldown followed by mass spectrometry identified the RNA binding protein hnRNPL as a key protein interacting with BRCAT431. Deletion studies identified a 27 base-pair region of BRCAT431 necessary for its interaction with hnRNPL, and loss of this region abrogated BRCAT431-induced invasion. Finally, guilt-by-association studies demonstrate a strong association between BRCAT431 overexpression and tumor grade, recurrence, and metastases. Conclusion: In this study, we develop the largest reported compendium of breast cancer lncRNAs. We prioritize BRCAT431 as the top lncRNA upregulated in ER-positive breast cancers, and demonstrate that it confers aggressive oncogenic phenotypes in vitro and in vivo. We identify a novel mechanism by which this lncRNA functions through interaction with hnRNPL. Our results suggest that by promoting tamoxifen resistance, this lncRNA increases the clinical risk of recurrence and metastases in breast cancer. Citation Format: Yashar Niknafs, Sumin Han, Teng Ma, James Rae, Arul Chinnaiyan, Felix Feng. Interrogation of the landscape of long noncoding RNAs in breast cancer to identify an ER-regulated predictor of tamoxifen resistance. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2662.

The non-coding landscape of head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is an aggressive disease marked by frequent recurrence and metastasis and stagnant survival rates. To enhance molecular knowledge of HNSCC and define a non-coding RNA (ncRNA) landscape of the disease, we profiled the transcriptome-wide dysregulation of long non-coding RNA (lncRNA), microRNA (miRNA), and PIWI-interacting RNA (piRNA) using RNA-sequencing data from 422 HNSCC patients in The Cancer Genome Atlas (TCGA). 307 non-coding transcripts differentially expressed in HNSCC were significantly correlated with patient survival, and associated with mutations in TP53, CDKN2A, CASP8, PRDM9, and FBXW7 and copy number variations in chromosomes 3, 5, 7, and 18. We also observed widespread ncRNA correlation to concurrent TP53 and chromosome 3p loss, a compelling predictor of poor prognosis in HNSCCs. Three selected ncRNAs were additionally associated with tumor stage, HPV status, and other clinical characteristics, and modulation of their expression in vitro reveals differential regulation of genes involved in epithelial-mesenchymal transition and apoptotic response. This comprehensive characterization of the HNSCC non-coding transcriptome introduces new layers of understanding for the disease, and nominates a novel panel of transcripts with potential utility as prognostic markers or therapeutic targets.

Abstract B19: Interrogation of the landscape of long noncoding RNAs in breast cancer to identify an ER-regulated predictor of tamoxifen resistance

Abstract Background: We have previously performed a bioinformatics-based analysis on 7,256 RNA sequencing libraries from tumors, normal tissues, and cell lines to delineate the landscape of long noncoding RNAs (lncRNAs) in the human transcriptome. This analysis identified 58,648 lncRNAs, including over 45,000 novel transcripts (Iyer and Niknafs et al, Nature Genetics, 2015). We now interrogate this lncRNA compendium to identify top candidate estrogen receptor (ER)-associated lncRNAs in breast cancer and characterize their association with disease progression. Methods: To identify and prioritize lncRNAs that were differentially expressed in cancer vs normal tissue, and in ER-positive vs ER-negative disease, we performed differential expression testing using a previously described non-parametric tool for RNA-seq count data called Sample Set Enrichment Analysis (SSEA) on over 1000 RNA sequencing libraries including breast cancer and normal tissue samples from the The Cancer Genome Atlas (TCGA) project. The effect of the top prioritized lncRNA on cancer phenotypes was studied via proliferation, colony formation, invasion and tamoxifen resistance assays in MCF7 and T47D cell lines, and via mouse xenograft studies in vivo. The mechanism by which this lncRNA promotes tumor progression was investigated by identifying its top protein interactors and its subdomains responsible for function, and then studying the effects of disrupting function of this lncRNA on cancer phenotypes. Finally, in a “guilt-by-association” study, we developed a signature of 150 protein coding genes most strongly associated with our lncRNA of interest, and investigated the association of this signature with tumor grade, recurrence, and metastases using Oncomine analyses. Results: Differential expression analyses on over 1000 TCGA RNA-seq samples nominated Breast Cancer Associated Transcript (BRCAT431) as the top overexpressed ER-regulated lncRNA in breast cancer. In vitro experiments demonstrate that shRNA-mediated knockdown of BRCAT431 resulted in significantly decreased proliferation, colony formation, and invasion (by >50% in most assays). Tamoxifen resistance was associated with significantly increased BRCAT431 levels in both MCF7 and T47D cells, and knockdown of BRCAT431 reversed tamoxifen resistance. In vivo xenograft studies demonstrate that knockdown of BRCAT431 also significantly decreased xenograft growth and tumor invasion by >50%. RNA pulldown followed by mass spectrometry identified the RNA binding protein hnRNPL as a key protein interacting with BRCAT431. Deletion studies identified a 27 base-pair region of BRCAT431 necessary for its interaction with hnRNPL, and loss of this region abrogated BRCAT431-induced invasion. Finally, guilt-by-association studies demonstrate a strong association between BRCAT431 overexpression and tumor grade, recurrence, and metastases. Conclusion: In this study, we develop the largest reported compendium of breast cancer lncRNAs. We prioritize BRCAT431 as the top lncRNA upregulated in ER-positive breast cancers, and demonstrate that it confers aggressive oncogenic phenotypes in vitro and in vivo. We identify a novel mechanism by which this lncRNA functions through interaction with hnRNPL. Our results suggest that by promoting tamoxifen resistance, this lncRNA increases the clinical risk of recurrence and metastases in breast cancer. Citation Format: Yashar S. Niknafs, Sumin Han, Teng Ma, Chao Zhang, Matthew K. Iyer, Rohit Malik, Anton Poliakov, Corey Speers, Daniel Hayes, James Rae, Felix Y. Feng, Arul M. Chinnaiyan. Interrogation of the landscape of long noncoding RNAs in breast cancer to identify an ER-regulated predictor of tamoxifen resistance. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr B19.

Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes.

The fungal kingdom is the source of almost all industrial enzymes in use for lignocellulose bioprocessing. We developed a systems-level approach that integrates transcriptomic sequencing, proteomics, phenotype, and biochemical studies of relatively unexplored basal fungi. Anaerobic gut fungi isolated from herbivores produce a large array of biomass-degrading enzymes that synergistically degrade crude, untreated plant biomass and are competitive with optimized commercial preparations from Aspergillus and Trichoderma. Compared to these model platforms, gut fungal enzymes are unbiased in substrate preference due to a wealth of xylan-degrading enzymes. These enzymes are universally catabolite-repressed and are further regulated by a rich landscape of noncoding regulatory RNAs. Additionally, we identified several promising sequence-divergent enzyme candidates for lignocellulosic bioprocessing.

Abstract S4-05: Interrogating the landscape of long noncoding RNAs in breast cancer to identify predictors of tamoxifen resistance

Abstract Background: We previously performed an informatics-based analysis on RNA sequencing libraries from 7,256 tumor and normal tissue specimens to delineate the landscape of long noncoding RNAs (lncRNAs) in the human transcriptome. This analysis identified 58,648 lncRNAs, including over 45,000 novel transcripts (Iyer MK et al, Nature Genetics, 2015). We now interrogate this lncRNA compendium to identify top candidate estrogen receptor (ER)-associated lncRNAs in breast cancer and characterize their association with disease progression. Methods: To prioritize differentially expressed lncRNAs in cancer vs normal tissue, and in ER+ vs ER- disease, we performed Sample Set Enrichment Analysis (SSEA) on >1000 RNA Seq libraries, from breast cancer and normal tissue samples from The Cancer Genome Atlas project. The effect of the top prioritized lncRNA on cancer phenotypes was studied via in vitro proliferation, colony formation, invasion and tamoxifen resistance assays in MCF7 and T47D cells, and via in vivo mouse xenograft studies and chick chorioallantoic membrane (CAM) assays. To study the mechanism by which this lncRNA promotes tumor progression, we identified its top protein interactors and subdomains responsible for function, and then studied the effects of disrupting function of this lncRNA on cancer phenotypes. Finally, in a "guilt-by-association" study, we developed a signature of 150 protein coding genes most strongly associated with our lncRNA of interest, and investigated the association of this signature with clinical outcomes using Oncomine analyses. Results: SSEA analysis on over 1000 TCGA samples nominated Breast Cancer Associated Transcript (BRCAT 431) as the top overexpressed ER-regulated lncRNA in breast cancer. In vitro experiments demonstrate that siRNA-mediated knockdown of BRCAT431 resulted in significantly decreased proliferation, colony formation, and invasion (by >50% in most assays). Tamoxifen resistance was associated with significantly increased BRCAT431 levels in both MCF7 and T47D cells, and knockdown of BRCAT431 reversed tamoxifen resistance. In vivo xenograft and CAM studies demonstrate that knockdown of BRCAT431 also significantly decreased xenograft growth and tumor invasion by >50%. RNA pulldown followed by mass spectrometry identified the RNA binding protein hnRNPL as a key protein interacting with BRCAT431. Deletion studies identified a 27 base region of BRCAT431 necessary for its interaction with hnRNPL, and loss of this region abrogated BRCAT431- induced invasion. Finally, guilt-by-association studies demonstrate a strong association between BRCAT431 overexpression and tumor grade, recurrence, and metastases. Conclusion: In this study, we develop the largest reported compendia of breast cancer lncRNAs. We prioritize BRCAT431 as the top lncRNA upregulated in ER-positive breast cancers, and demonstrate that it confers aggressive oncogenic phenotypes in vitro and in vivo. We identify a novel mechanism by which this lncRNA functions. Our results suggest that by promoting tamoxifen resistance, BRCAT431 increases the clinical risk of recurrence and metastases in breast cancer. Overall, this study supports the rationale for investigating lncRNAs as novel biomarkers and therapeutic targets in breast cancer. Citation Format: Feng FY, Niknafs Y, Han S, Ma T, Speers C, Malik R, Evans J, Zhang C, Pierce LJ, Hayes DF, Rae JM, Chinnaiyan AM. Interrogating the landscape of long noncoding RNAs in breast cancer to identify predictors of tamoxifen resistance. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr S4-05.

Landscape of Long Noncoding RNAs in Psoriatic and Healthy Skin

We used RNA sequencing to study and characterize the long noncoding RNA (lncRNA) transcriptome in lesional skin from psoriasis patients before (PP) and after treatment (PT) with adalimumab and in normal skin from healthy individuals (NN). To this end, we sequenced total RNA from 18 psoriasis patients and 16 healthy controls. We merged three lncRNA reference datasets to create a single combined reference of 67,157 lncRNA transcripts with no overlaps. We identified differential expression of 971 lncRNAs between PP and NN, 157 between PP and PT, and 377 between PT and NN. Using differentially expressed lncRNAs between PP and NN, we identified a molecular lncRNA signature that distinguishes psoriatic skin from healthy skin. Furthermore, we performed an unsupervised hierarchical analysis that revealed distinct clustering of PP samples from NN. Acoding noncoding network analysis revealed a large network of highly correlated lncRNA and protein coding transcripts that provided insight into the potential functions of unannotated lncRNAs. To the best of our knowledge, this description of both polyadenylated as well as nonpolyadenylated lncRNA transcripts in psoriasis has not been previously reported. Our findings highlight the potential importance of lncRNAs in the biology of psoriasis and response to treatment.

Integrated Analysis of the Human Hematopoietic Non-Coding RNA Landscape Reveals Lnc-RNA Stem Cell Signature in AML

Long non-coding RNAs (lncRNAs) and miRNAs have emerged as crucial regulators of gene expression, epigenetics and cell fate decisions. Here we present an integrated quantitative and functional analysis of the miRNA-, lncRNA- and mRNA-transcriptome of purified human hematopoietic stem cells (HSCs) and their differentiated progenies, including granulocytes, monocytes, T-cells, NK-cells, B-cells, megakaryocytes and erythroid precursors, which we correlated with the ncRNA expression profile of 46 pediatric AML samples to establish a core lncRNA stem cell signature in AML.For each blood cell population, RNA from 5 healthy donors was hybridized onto three microarray platforms (Arraystar lncRNA V2.0, NCode™-miRNA/-ncRNA), yielding a coverage of more than 40,000 lncRNAs, 25,000 mRNAs and 900 miRNAs on 146 arrays. Compared to mRNAs, the mean expression level of lncRNAs was nearly 2-fold lower (p<2.2x10-16), highlighting the challenge for RNA-Seq to provide adequate coverage of these rare transcripts. Hierarchical clustering, PCA (principle components analysis) and t-SNE (t-distributed stochastic neighbor embedding) on lncRNA and miRNA genes robustly structured the dataset into groups of samples that matched the input populations, demonstrating their unique ncRNA expression profiles. Using LIMMA (linear models for microarrays) and PAM (prediction analysis of microarrays) we extracted a robust lncRNA/miRNA core signature for each population. Self-organizing maps (SOM) structured the gene space into spot clusters of lncRNAs and mRNAs that were coordinately expressed during maturation from HSCs into the different lineages. Using a guilt-by-association approach, we assigned potential functionality to lncRNAs through gene set enrichment analysis of co-expressed mRNAs (validated for a set of lncRNAs by qRT-PCR). To demonstrate their functionality, we knocked down two lncRNAs (LINC00173 and DY635655) from the granulocytic core signature using two independent shRNA constructs, which resulted in diminished granulocytic in vitro differentiation (2-fold reduction in percentage of CD66b+/CD13+ granulocytes, p≤0.05), myeloid colony-formation (1.5-2-fold, p≤0.05) and nuclear lobulation (MGG-staining). Accordingly, CRISPR-mediated transcriptional repression of nuclear localized LINC00173 and cytoplasmic localized DY635655 (RNA-FISH validated by qRT-RNA of fractionated RNA) using dCas9-KRAB and three sgRNAs per locus reduced proliferation of myeloid NB4 cells (2-3-fold, p≤0.01).By plotting ncRNA expression over the genomic coordinates of all chromosomes, we uncovered a strong and highly coordinated upregulation of miRNAs, small nucleolar RNAs (snoRNAs) and lncRNAs (MEG3, MEG8 and MEG9) within the DLK1-DIO3 locus on chromosome 14 (hsa14) specifically in megakaryocytes (average log2-FC >5, p<10-8; confirmed by qRT-PCR). shRNA-mediated knock-down of MEG3 reduced erythroid colony-formation and megakaryocytic cell proliferation in vitro (p≤0.05), implicating the functional importance of this ncRNA locus.Having established a global human hematopoietic lncRNA expression resource, we extended our findings to malignant hematopoiesis. Linear (PCA) and nonlinear (t-SNE) dimensionality reduction of 46 pediatric AML samples including Down syndrome AMKL, core-binding factor AMLs (inv[16] or t[8;21]) and MLL-rearranged leukemias mapped most samples to a space between HSCs and differentiated cells together with the myeloid progenitors. A subset of AML-samples mapped closely to healthy HSCs, including most of the DS-AMKLs and MLL-AMLs. We identified a stem-cell associated lncRNA signature that was absent in healthy differentiated progenies, but upregulated in AML samples. Interestingly, AML samples that did not show upregulation of this set of lncRNAs suppressed the upregulation of differentiation-associated lncRNAs, thereby keeping the global identity of myeloid progenitors as shown by t-SNE.Thus, our study provides a comprehensive resource for the exploration of the mRNA-, lncRNA- and miRNA-transcriptome across the human hematopoietic hierarchy, including malignant hematopoiesis. The definition of a core lncRNA stem cell signature in normal HSCs and AML blasts will guide our way towards an improved understanding of self-renewal and the underlying transcriptional program, which is hijacked during malignant transformation. DisclosuresNo relevant conflicts of interest to declare.

Abstract 2992: The landscape of long non-coding RNAs in cancer

Abstract Long non-coding RNAs (lncRNAs) are emerging as important players in cancer. The emergence of high-throughput RNA sequencing (RNA-Seq) technology provides a revolutionary means for systematic discovery of transcribed elements. However, efforts to catalog lncRNAs have thus far failed to appreciate the transcriptional robustness and complexity in the human genome because they were confined to select cell lines or small numbers of mostly normal tissue. In order to delineate genome-wide lncRNA expression, we curated 7,256 RNA-Seq libraries comprising over 43 terabases of sequence from 25 independent studies including tumors, normal tissues, and cell lines. The data were processed using ab initio transcriptome meta-assembly methodologies to develop a consensus of 91,013 expressed genes. Over 68% (58,648) of genes were classified as lncRNAs, of which 79% (48,952) were previously unannotated. About 1% (597) of the lncRNAs harbor ultraconserved elements and 7% (3,900) overlap disease-associated single nucleotide polymorphisms (SNPs). To prioritize lineage-specific, disease-associated lncRNA expression, we developed a non-parametric method for differential expression testing called Sample Set Enrichment Analysis (SSEA), and nominated 7,942 lineage- or cancer-associated lncRNA genes. In order to interrogate potential functionality for these lncRNAs, gene set enrichment testing was performed on genes correlated with each lncRNA. By uncovering this expansive landscape of cancer-associated lncRNAs, we provide the scientific community a powerful starting point to begin investigating their biological relevance. We anticipate that the lncRNAs identified by this study, as well as the computational tools developed herein, will provide a foundation for lncRNA genomics, biomarker development, and the delineation of cancer disease mechanisms. Citation Format: Yashar S. Niknafs, Matthew K. Iyer, Arul M. Chinnaiyan. The landscape of long non-coding RNAs in cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2992. doi:10.1158/1538-7445.AM2015-2992

The long intergenic noncoding RNA landscape of human lymphocytes highlights the regulation of T cell differentiation by linc-MAF-4.

Long non-coding-RNAs are emerging as important regulators of cellular functions but little is known on their role in human immune system. Here we investigated long intergenic non-coding-RNAs (lincRNAs) in thirteen T and B lymphocyte subsets by RNA-seq analysis and de novo transcriptome reconstruction. Over five hundred new lincRNAs were identified and lincRNAs signatures were described. Expression of linc-MAF-4, a chromatin-associated TH1-specific lincRNA, was inversely correlated with MAF, a TH2-associated transcription factor. Linc-MAF-4 down-regulation skewed T cell differentiation toward TH2. We identified a long-distance interaction between linc-MAF-4 and MAF genomic regions, where linc-MAF-4 associates with LSD1 and EZH2, suggesting linc-MAF-4 regulated MAF transcription by recruitment of chromatin modifiers. Our results demonstrate a key role of lincRNAs in T lymphocyte differentiation.

The landscape of long noncoding RNAs in the human transcriptome.

Long noncoding RNAs (lncRNAs) are emerging as important regulators of tissue physiology and disease processes including cancer. To delineate genome-wide lncRNA expression, we curated 7,256 RNA sequencing (RNA-seq) libraries from tumors, normal tissues and cell lines comprising over 43 Tb of sequence from 25 independent studies. We applied ab initio assembly methodology to this data set, yielding a consensus human transcriptome of 91,013 expressed genes. Over 68% (58,648) of genes were classified as lncRNAs, of which 79% were previously unannotated. About 1% (597) of the lncRNAs harbored ultraconserved elements, and 7% (3,900) overlapped disease-associated SNPs. To prioritize lineage-specific, disease-associated lncRNA expression, we employed non-parametric differential expression testing and nominated 7,942 lineage- or cancer-associated lncRNA genes. The lncRNA landscape characterized here may shed light on normal biology and cancer pathogenesis and may be valuable for future biomarker development.

Classifying endometrioid endometrial cancer by long noncoding RNA profiling: Indication of prognosis and therapy selection.

11064 Background: The Cancer Genome Atlas (TCGA) has recently reported an integrative analysis of endometrioid endometrial carcinoma (EEC) . However, the landscape of long non-coding RNAs (lncRNA) and its role in defining tumor subtypes with clinical relevance have not been addressed. Methods: Using TCGA molecular RNAseq profiles related to 304 primary endometrioid endometrial tumors (serous and mixed tumors were excluded), we performed genomic analysis of GENCODE lncRNAs. Furthermore, we established lncRNA expression-based subtypes with distinct signatures, and described global correlations of lncRNA expression-based subtypes with genomic aberrations and clinico-pathological tumor features. Results: Using stringent criteria, we identified 1,931 lncRNAs and predicted those that are potential drivers through integrative analysis. Unsupervised clustering of lncRNA expression revealed three robust subtypes, one of which displayed a worse overall survival as compared to the other two (Cluster C3, p=0.029). Pa...

Abstract 1824: The non-coding RNA landscape of lung, liver and breast cancer reveals novel tumor-associated ncRNAs, concerted regulation and unexpected tissue specificity.

Abstract Non-coding RNA profiles in cancer are largely unknown which greatly impedes the discovery of functionally important ncRNAs in tumorigenesis as well as the generation of genome-wide libraries. Here, we define the ncRNA expression landscape of lung, breast and liver cancer as well as normal tissue from the respective organs in a large set of primary patient samples (N=150). These samples were carefully selected to have a long patient follow-up and complete clinical datasets to allow an in-depth analysis. We provide the first comprehensive map of 17000+ long ncRNAs in a broad range of human tumor and normal tissues and discovered hundreds of new ncRNAs associated with three major tumor entities. Importantly, we uncovered that ncRNA profiles are significantly more specific to the tissue of origin than patterns of protein-coding mRNAs. Also, the significant ncRNA signatures in this study demonstrate specific ncRNA patterns that are unlikely to be transcriptional background but rather the result of concerted regulation. To mimic the response to cytotoxic chemotherapy, we have also treated lung and liver cancer cells with the DNA damaging agents Cisplatin and Etoposide and found significant deregulation of long ncRNAs - reversing in part the differences seen between normal and malignant lung tissue. Importantly, one of the Cisplatin-regulated ncRNAs interacts with DNA repair factors linking it immediately to the DNA damage response. To elucidate the molecular functions of the novel ncRNAs, we used RNA affinity purification to identify the protein interaction partners. In addition, we create human knockout cells for lncRNAs using ZFN and TALEN technology to integrate RNA destabilizing elements into the human genome which yields more than 1000-fold lncRNA silencing. Our so far largest ncRNA expression map is also exploited in another way: We generate an siRNA library specifically targeting over 600 tumor-associated ncRNAs based on our profiling landscape. This comprehensive but focused library will elucidate the role of ncRNAs in tumorigenesis, viability, apoptosis and the DNA damage response. In summary, we provide the first global comprehensive map of long ncRNA expression in a broad range of human tumor and normal tissue samples and discovered many new lncRNAs associated with cancer as well as tissue-, histology- and prognosis-specific ncRNA signatures. Citation Format: Maria Polycarpou-Schwarz, Tony Gutschner, Monika Hämmerle, Anna Roth, Ashish Goyal, Stefanie Grund, Catherina Hildenbrand, Arne Warth, Thomas Longerich, Sebastian Aulmann, Joachim Rom, Michael Meister, Thomas Muley, Heike Zabeck, Sabine Schmidt, Tomi Ivacevic, Vladimir Benes, Kai Breuhahn, Philipp Schnabel, Peter Sinn, Hans Hoffmann, Peter Schirmacher, Sven Diederichs. The non-coding RNA landscape of lung, liver and breast cancer reveals novel tumor-associated ncRNAs, concerted regulation and unexpected tissue specificity. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1824. doi:10.1158/1538-7445.AM2013-1824

Describing the structural robustness landscape of bacterial small RNAs

BackgroundThe potential role of RNA molecules as gene expression regulators has led to a new perspective on the intracellular control and genome organization. Because secondary structures are crucial for their regulatory role, we sought to investigate their robustness to mutations and environmental changes.ResultsHere, we dissected the structural robustness landscape of the small non-coding RNAs (sncRNAs) encoded in the genome of the bacterium Escherichia coli. We found that bacterial sncRNAs are not significantly robust to both mutational and environmental perturbations when compared against artificial, unbiased sequences. However, we found that, on average, bacterial sncRNAs tend to be significantly plastic, and that mutational and environmental robustness strongly correlate. We further found that, on average, epistasis in bacterial sncRNAs is significantly antagonistic, and positively correlates with plasticity. Moreover, the evolution of robustness is likely dependent upon the environmental stability of the cell, with more fluctuating environments leading to the emergence and fixation of more robust molecules. Mutational robustness also appears to be correlated with structural functionality and complexity.ConclusionOur study provides a deep characterization of the structural robustness landscape of bacterial sncRNAs, suggesting that evolvability could be evolved as a consequence of selection for more plastic molecules. It also supports that environmental fluctuations could promote mutational robustness. As a result, plasticity emerges to link robustness, functionality and evolvability.

A non-biased framework for the annotation and classification of the non-miRNA small RNA transcriptome

Recent progress in high-throughput sequencing technologies has largely contributed to reveal a highly complex landscape of small non-coding RNAs (sRNAs), including novel non-canonical sRNAs derived from long non-coding RNA, repeated elements, transcription start sites and splicing site regions among others. The published frameworks for sRNA data analysis are focused on miRNA detection and prediction, ignoring further information in the dataset. As a consequence, tools for the identification and classification of the sRNAs not belonging to miRNA family are currently lacking. Here, we present, SeqCluster, an extension of the currently available SeqBuster tool to identify and analyze at different levels the sRNAs not annotated or predicted as miRNAs. This new module deals with sequences mapping onto multiple locations and permits a highly versatile and user-friendly interaction with the data in order to easily classify sRNA sequences with a putative functional importance. We were able to detect all known classes of sRNAs described to date using SeqCluster with different sRNA datasets.