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      Unique Signatures of Long Noncoding RNA Expression in Response to Virus Infection and Altered Innate Immune Signaling

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          Abstract

          Studies of the host response to virus infection typically focus on protein-coding genes. However, non-protein-coding RNAs (ncRNAs) are transcribed in mammalian cells, and the roles of many of these ncRNAs remain enigmas. Using next-generation sequencing, we performed a whole-transcriptome analysis of the host response to severe acute respiratory syndrome coronavirus (SARS-CoV) infection across four founder mouse strains of the Collaborative Cross. We observed differential expression of approximately 500 annotated, long ncRNAs and 1,000 nonannotated genomic regions during infection. Moreover, studies of a subset of these ncRNAs and genomic regions showed the following. (i) Most were similarly regulated in response to influenza virus infection. (ii) They had distinctive kinetic expression profiles in type I interferon receptor and STAT1 knockout mice during SARS-CoV infection, including unique signatures of ncRNA expression associated with lethal infection. (iii) Over 40% were similarly regulated in vitro in response to both influenza virus infection and interferon treatment. These findings represent the first discovery of the widespread differential expression of long ncRNAs in response to virus infection and suggest that ncRNAs are involved in regulating the host response, including innate immunity. At the same time, virus infection models provide a unique platform for studying the biology and regulation of ncRNAs.

          IMPORTANCE

          Most studies examining the host transcriptional response to infection focus only on protein-coding genes. However, there is growing evidence that thousands of non-protein-coding RNAs (ncRNAs) are transcribed from mammalian genomes. While most attention to the involvement of ncRNAs in virus-host interactions has been on small ncRNAs such as microRNAs, it is becoming apparent that many long ncRNAs (>200 nucleotides [nt]) are also biologically important. These long ncRNAs have been found to have widespread functionality, including chromatin modification and transcriptional regulation and serving as the precursors of small RNAs. With the advent of next-generation sequencing technologies, whole-transcriptome analysis of the host response, including long ncRNAs, is now possible. Using this approach, we demonstrated that virus infection alters the expression of numerous long ncRNAs, suggesting that these RNAs may be a new class of regulatory molecules that play a role in determining the outcome of infection.

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          Fast and reliable prediction of noncoding RNAs.

          We report an efficient method for detecting functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, already has yielded excellent results for a small number of aligned sequences and is suitable for large-scale genomic screens. It consists of two basic components: (i) a measure for RNA secondary structure conservation based on computing a consensus secondary structure, and (ii) a measure for thermodynamic stability, which, in the spirit of a z score, is normalized with respect to both sequence length and base composition but can be calculated without sampling from shuffled sequences. Functional RNA secondary structures can be identified in multiple sequence alignments with high sensitivity and high specificity. We demonstrate that this approach is not only much more accurate than previous methods but also significantly faster. The method is implemented in the program rnaz, which can be downloaded from www.tbi.univie.ac.at/~wash/RNAz. We screened all alignments of length n > or = 50 in the Comparative Regulatory Genomics database, which compiles conserved noncoding elements in upstream regions of orthologous genes from human, mouse, rat, Fugu, and zebrafish. We recovered all of the known noncoding RNAs and cis-acting elements with high significance and found compelling evidence for many other conserved RNA secondary structures not described so far to our knowledge.
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            Experimental validation of the regulated expression of large numbers of non-coding RNAs from the mouse genome.

            Recent large-scale analyses of mainly full-length cDNA libraries generated from a variety of mouse tissues indicated that almost half of all representative cloned sequences did not contain an apparent protein-coding sequence, and were putatively derived from non-protein-coding RNA (ncRNA) genes. However, many of these clones were singletons and the majority were unspliced, raising the possibility that they may be derived from genomic DNA or unprocessed pre-mRNA contamination during library construction, or alternatively represent nonspecific "transcriptional noise." Here we show, using reverse transcriptase-dependent PCR, microarray, and Northern blot analyses, that many of these clones were derived from genuine transcripts of unknown function whose expression appears to be regulated. The ncRNA transcripts have larger exons and fewer introns than protein-coding transcripts. Analysis of the genomic landscape around these sequences indicates that some cDNA clones were produced not from terminal poly(A) tracts but internal priming sites within longer transcripts, only a minority of which is encompassed by known genes. A significant proportion of these transcripts exhibit tissue-specific expression patterns, as well as dynamic changes in their expression in macrophages following lipopolysaccharide stimulation. Taken together, the data provide strong support for the conclusion that ncRNAs are an important, regulated component of the mammalian transcriptome.
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              Genomic and Transcriptional Co-Localization of Protein-Coding and Long Non-Coding RNA Pairs in the Developing Brain

              Introduction The mammalian genome displays a complex and extensive pattern of interlaced transcription of protein-coding genes and thousands of non-coding RNA (ncRNA; see Materials and Methods for definitions) loci [1]. Exons from ncRNA loci may overlap on the same (sense), or opposite (antisense), strand with exons from other transcripts, including those from protein-coding genes. They may also be contained within introns of other transcripts. Other ncRNAs are transcribed from bidirectional promoters: their transcriptional events, and those for neighbouring transcripts from the opposite strand, are initiated in close genomic proximity. Several recent studies investigated whether cis-antisense, intronic, or bidirectional ncRNAs regulate the transcription of protein-coding genes whose loci they overlap [2],[3]. These report complex relationships between the expression profiles of ncRNAs and their overlapping protein-coding genes in adult mice. Further investigations, however, are clearly needed to investigate other types of ncRNAs, in particular intergenic and long (>200 nt) ncRNAs transcribed from outside protein-coding loci, and those expressed during development. If most long ncRNAs convey biological functions, then what these molecular mechanisms are remain almost completely unknown. For the few with established mechanisms a general theme has emerged of them acting as transcriptional regulators of protein-coding genes (reviewed in [4]). For many such ncRNAs, the genomic location of their transcription has proved key to their mechanism. When promoters of non-coding and coding transcripts are closely juxtaposed on the chromosome, for example, then transcriptional events initiated from them may be coupled. This has been shown to occur following chromatin remodelling of chromosomal domains [5]–[7], or because of collisions between transcriptional machineries processing along sequence in close proximity [8], or because of transcriptional interference when transcription proceeds through a promoter sequence thereby suppressing transcription initiation from it [8]. Other long ncRNAs are cis-regulators of transcription via indirect means involving their participation in ribonucleoprotein complexes [9],[10]. Other long ncRNAs, such as NRON or 7SK, act in trans: they regulate the expression of target genes or gene products from chromosomes other than the ones from which they are transcribed [11]–[13]. Cis-regulation by ncRNAs of protein-coding gene transcription is well-established in imprinting [14] and for developmental genes, such as Dlx5 and Dlx6 [9], yet these represent transcriptional events that overlap on the genome. By way of contrast, we sought statistical evidence that pairs of adjacent, yet distinct, coding and non-coding loci often give rise to separate transcripts with similar spatiotemporal expression patterns indicative of positive co-operativity of transcriptional regulation. (Of course, negative co-operativity by, for example, transcriptional interference is also likely. However, such instances tend to be harder to establish experimentally owing to low levels of ncRNA expression.) We considered that if evidence of transcriptional co-operativity were to be forthcoming then specific pairs of coding and noncoding transcripts could be prioritised for experimentation. In such studies, it is important to demonstrate that long ncRNAs and mRNAs are transcribed exclusively from separate promoters. Otherwise, similarities in their expression profiles may not represent distinct transcriptional events but instead single transcripts spanning both coding and noncoding exons. We recently demonstrated several evolutionary signatures of functionality for a large set of mouse long ncRNAs and their promoters [15]. These long ncRNA sequences are largely full-length [16], map to genomic loci lying outside of protein-coding gene models and consequently are unlikely to act as antisense transcripts of a neighbouring gene locus. Although some of these ncRNAs may result from uncoordinated and inconsequential transcription, evidence of transcriptional regulation [17] and constraints on splicing motifs [15] cannot be explained by such transcriptional ‘noise’. We were interested in whether long intergenic ncRNAs are located randomly with respect to protein-coding genes. If not, this might suggest a trend for long ncRNAs to act in cis with neighbouring protein-coding genes. To improve our chances of detecting non-uniformities of chromosomal location, we considered long ncRNAs whose genomic sequences are evolutionarily constrained and thus are more likely to be functional. If long ncRNAs possess, in general, cis-regulatory roles, one might expect their transcribed genomic regions to lie in proximity to their functionally-linked protein-coding genes, and their tissue expression profiles to be similar. Finally, it might also be expected that functional long ncRNAs would tend to be linked to certain subsets of protein-coding genes that convey particular biological functions. We investigated this cis-regulatory hypothesis for a set of 659 evolutionary constrained long ncRNAs and found large-scale and experimental evidence for co-regulation of non-coding and protein-coding transcript pairs. For the first time, we show that these constrained long ncRNAs are not evenly distributed on the genome but rather tend to be concentrated near to genes with similar expression patterns and from particular functional classes. These findings immediately provide new and unbiased criteria for prioritising long ncRNAs for experimental investigation. Hundreds of constrained long ncRNAs can now be targeted for detailed examination, specifically those that either (i) are expressed in the brain during development and are transcribed in proximity to transcription factor genes, or (ii) are expressed outside of the CNS in adult individuals and that lie adjacent to signalling genes. Results This study examined large numbers of mouse long intergenic ncRNAs, partitioned by the availability or otherwise of evidence for their expression in the brain or during development, and of evidence for sequence constraint. Previous studies had focused specifically on the expression of antisense, bidirectional and intronic ncRNAs in 56 day old adult mice or during mouse embryonic stem cell differentiation [2],[3]. For each set of ncRNA loci we examined the null hypothesis that they are located at random relative to protein-coding genes. Instead, we find strong and significant co-expression and functional biases. We show experimentally that these biases do not derive from single transcriptional events. Constrained ncRNAs are enriched in predicted RNA secondary structures We started by analysing 3,122 long ncRNAs transcribed from intergenic regions (see Materials and Methods) that, when considered together, exhibit evolutionary constraint [15]. Among these ncRNAs, we then identified 659 long ncRNAs that individually show evidence of constraint (hereafter termed constrained long ncRNAs): individually, their mouse-human nucleotide substitution rate is significantly (p 9 tags). (0.03 MB DOCX) Click here for additional data file. Figure S3 Co-expression of further protein-coding/non-coding RNA transcript pairs in the developing (Panels A, B, C) and adult (Panels D, E, F) CNS. Brightfield images of in situ hybridization from adjacent wild-type sections are shown. (A) Expression of the ncRNA AK082989 appeared ubiquitous in an E13.5 embryo, although Zic4, the adjacent protein coding gene, showed a highly specific pattern of expression in the spinal cord and forebrain at the same time-point, as was described previously (Gaston-Massuet et al., 2005). (B) At P12, Meis1 is only expressed above background levels in the developing cerebellar granule cell layer, where the ncRNA AK042766 is also found expressed. (C) Grik2, however, is expressed ubiquitously in the brain, although the adjacent ncRNA AK047467 is only found at low levels in the cerebellar granule cell layer at P12. (D) Both Hip2 and its paired ncRNA, AK045758, are expressed at high levels in the cortex and the hippocampus. (E) Eif2c3 is ubiquitously expressed in the brain, as is the genomically adjacent transcribed ncRNA, AK047638. (F) Adr also shows a ubiquitous expression pattern, although expression of its paired ncRNA, AK162901, is not detected in the adult brain, consistent with the RT-PCR results (Figure S3). In all cases, the sense strand negative control probe failed to show specific staining (data not shown). Gaston-Massuet, C, Henderson DJ, Greene ND, Copp AJ, (2005) Zic4, a zinc-finger transcription factor, is expressed in the developing mouse nervous system. Dev Dyn, 233: 1110-5. (3.43 MB TIF) Click here for additional data file. Figure S4 RT-PCR and 5′ RACE analysis of protein-coding and non-coding transcripts. (A) Total RNA was purified from the tissues and the developmental time-points indicated. RT-PCR was performed using primers spanning from the 3′ UTR of the protein-coding gene to the adjacent ncRNA genomic sequence. Control amplification using the same primer pairs from genomic DNA (gDNA) and a reaction containing no reverse transcriptase (-RT) is also shown. Importantly, RT-PCR of each protein-coding gene and ncRNA was performed from the same tissue. Apart from Add2/AK013768, no evidence for read-through from the 3′ UTR to the ncRNA was observed that would account for the in situ hybridisation results obtained (Figure 5, Figure 6). (B) 5′ RACE products of all 12 ncRNAs analysed in this study (adjacent pc genes are indicated in brackets). Total RNA was purified from the tissue corresponding to the in situ hybridisation data: adult brain (AK018196 - AK162901), P12 cerebellum (AK149041, AK042766 and AK047467) and E13.5 brain (AK082938, AK049627 and AK082969). In these reactions, a nested reverse primer approximately 300 bp from the predicted ncRNA transcription start site and a nested forward primer specific for the cap-ligated RACE anchor primer was used. A reaction containing no reverse transcriptase (-RT) is also shown for each primer pair. RACE reactions containing no TAP enzyme showed no amplification products (data not shown). (1.16 MB TIF) Click here for additional data file. Table S1 Brain-expressed ncRNAs are more likely to be constrained than ncRNAs expressed elsewhere (χ2-test, p = 3×10−3). This observed bias is independent of the lengths of these constrained ncRNAs since the length distributions of brain- and non-brain-expressed ncRNAs are indistinguishable (p = 0.4, Kolmogorov-Smirnov test). Transcripts classified as constrained or non-constrained were divided further into those transcribed in the same (sense) or opposite (antisense) direction relative to the transcriptional orientation of the most proximal protein-coding gene. Cases where a ncRNA is located near to protein-coding genes that are transcribed on both strands have been excluded. An asterisk (*) indicates a significant association with the direction of transcription of the proximal annotated protein-coding gene (see Materials and Methods). Non-constrained, brain-expressed ncRNAs show no directional preference, whereas non-brain-expressed ncRNAs show a small but significant bias in the opposite orientation (54% transcribed in antisense, p = 6×10−3). (0.03 MB XLS) Click here for additional data file. Table S2 Constrained ncRNAs that are expressed in brain or in nonbrain tissues during development show a significant tendency to lie adjacent to proteincoding genes that are highly expressed in specific tissues (p<10−3; EFDR<0.04). Shown is the significant over-representation of ncRNAs in proximity to protein-coding genes that are expressed in these tissues as a result of the observed densities when compared to expected densities on randomly sampled G+C matched sequences; also shown are the lower and upper confidence intervals (CIs) at the 95% level and the standard deviation. (0.02 MB XLS) Click here for additional data file. Table S3 Brain-expressed and constrained ncRNAs show a tendency to be transcribed near to protein-coding genes expressed in brain tissues. Shown are significant (p-value<10−2, EFDR = 0.53) and non-significant (highlighted in grey) enrichments. The observed densities of ncRNAs transcribed in proximity to protein-coding genes expressed in particular tissues have been compared to expected densities from randomly sampled G+C matched sequences (see Materials and Methods). Also shown are lower and upper confidence intervals (CIs) at the 95% level, and standard deviations (StdDev). Terms highlighted in bold correspond to results shown in Figure 4 (p-value<10−3, EFDR = 0.05). (0.02 MB XLS) Click here for additional data file. Table S4 Experimental EST and CAGE TC (tag cluster) support for six non-coding transcripts (AK018196, AK045528, AK013768, AK149041, AK082938, AK049627) for which in situ hybridizations (ISHs) were performed (see Figure 5, Figure 6). Each of the six brain-derived and evolutionarily constrained ncRNA transcripts was further investigated for additional experimental evidence in the form of ESTs and CAGE TCs and the results are summarized in separate tables. For each EST and CAGE TC, its accession code, coordinates, strand, tissue type and stage are reported, and additionally for each EST its position (5′ or 3′) relative to the ncRNA is shown. (0.08 MB XLS) Click here for additional data file. Table S5 ncRNA data sets used in this study: evolutionary and functional properties. The four sets contain ncRNAs that are (i) constrained and derived from brain-associated tissues, (ii) constrained and derived from tissues outside the CNS, (iii) non-constrained and derived from brain-associated tissues and (iv) non-constrained and derived from tissues outside the CNS. Each ncRNA is represented by its (i) accession code, (ii) genome coordinates (assembly mm5), (iii) strand information and (iv) whether it overlaps with: 1. EvoFold predictions of RNA secondary structure (EvoFold), 2. human copy number variants (CNVs), 3. segmental duplications (SDs), 4. PhastCons multispecies conserved elements (MCSs), and 5. indelpurified segments (IPSs). Overlap is indicated by the integer 1, lack of overlap by 0. (0.35 MB XLS) Click here for additional data file. Table S6 ncRNA data sets used in this study: accession codes of all ncRNAs in these four data sets. The four sets contain ncRNAs that are (i) constrained and derived from brain-associated tissues, (ii) constrained and derived from tissues outside the CNS, (iii) non-constrained and derived from brain-associated tissues and (iv) non-constrained and derived from tissues outside the CNS. In particular, the two unconstrained data sets are listed in their entireties since in Table S5 only those that are homologous to human sequence are shown. (0.16 MB XLS) Click here for additional data file. Text S1 Functional associations and transcript read-through. (0.03 MB DOC) Click here for additional data file.
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                Author and article information

                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society of Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                26 October 2010
                Nov-Dec 2010
                : 1
                : 5
                : e00206-10
                Affiliations
                Department of Microbiology, School of Medicine, University of Washington, Seattle, Washington, USA [a ];
                Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA [b ];
                Department of Molecular Informatics, Rosetta Inpharmatics LLC, Seattle, Washington, USA [c ];
                Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA [d ];
                Department of Microbiology and Immunology, University of Maryland, Baltimore, Maryland, USA [e ]; and
                Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, North Carolina, USA [f ]
                Author notes
                Address correspondence to Michael G. Katze, honey@ 123456u.washington.edu .
                [*]

                Present address: Christopher D. Armour, Matthew C. Biery, and Christopher K. Raymond, NuGEN Technologies, Inc., Research and Development, San Carlos, California, USA; John C. Castle, Institute for Translational Oncology and Immunology, Mainz, Germany; Heather K. Bouzek, Department of Microbiology, University of Washington, Seattle, Washington, USA; David R. Haynor, Department of Radiology, University of Washington, Seattle, Washington, USA.

                M.J.T. performed qPCR experiments. X.P., S.P., M.J.T., and C.D.A. analyzed the data. X.P., C.D.A., S.P., J.C.C., M.H., C.K.R., R.S.B., and M.G.K. contributed to the concept, strategy, study design, and project management. X.P., L.G., C.D.A., M.F., B.G.B.-T., M.J.T., M.J.K., R.S.B., and M.G.K. wrote the manuscript.

                Editor Terence Dermody, Vanderbilt University Medical Center

                Article
                mBio00206-10
                10.1128/mBio.00206-10
                2962437
                20978541
                8a544963-311f-4e81-b9d0-4bc09684e6a1
                Copyright © 2010 Peng et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 August 2010
                : 29 September 2010
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                Life sciences
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