RNA sequencing reads of A. fumigatus strains were mapped against the A. fumigatus Af293 reference genome. Unmapped reads were collected for de novo assembly. Contigs were analyzed by Blastx comparison with a mycovirus protein database. Assembled viral genomes were used as template for remapping of RNA sequencing reads. In total, deep RNA sequencing results from 11 A. fumigatus strains were analyzed for the presence of mycoviral genomic RNAs. In 9 out of 11 strains, putative mycoviral RNA genomes were identified. Three strains were infected with two different mycovirus species. Two strains were infected with Aspergillus fumigatus polymycovirus type-1 (AfuPmV-1). Four strains contained fully recovered genomic RNA of unknown narna-like viruses designated as Aspergillus fumigatus narnavirus-1 and Aspergillus fumigatus narnavirus-2 (AfuNV-1 and AfuNV-2). Both viruses showed 38% amino acid sequence identity to Beihai narna-like virus-21. Three strains contained partially recovered genomic RNA of an unknown narna-like virus. Two strains contained fully recovered genomic RNAs of an unknown partitivirus designated as Aspergillus fumigatus partitivirus-2 (AfuPV-2) which showed 50% amino acid sequence identity to Alternaria alternata partitivirus-1. Finally, one strain contained fully recovered genomic RNA of an unknown mitovirus designated as Aspergillus fumigatus mitovirus-1 (AfuMV-1) which showed 34% amino acid sequence identity to Sclerotina sclerotiorum mitovirus. In silico analysis of deep RNA sequencing results showed that a majority of the A. fumigatus strains used here were infected with mycoviruses. Four novel A. fumigatus RNA mycoviruses could be identified: two different Aspergillus fumigatus narna-like viruses, one Aspergillus fumigatus partitivirus, and one Aspergillus fumigatus mitovirus.In the last few years, increasing numbers of viruses infecting fungi have been identified. In this study, we used an  Aspergillus [Recent studies have shown that mycoviruses are widespread among all major fungal taxa, including ergillus –4. In thergillus , 5, 6. Bergillus . The majergillus . Double-Little is known about the transmission of mycoviruses. Especially mycoviruses with RNA genomes are generally considered as endogenous viruses without an extracellular stage during virus replication. However, it was reported that Gemycircularvirus, a circular ssDNA virus, is transmitted via infectious particles . AlthougAspergillus fumigatus. In 9 out of 11 strains tested in this study, we found RNAs from six different mycoviral species. In this paper, we describe the molecular discovery and characterization of four novel A. fumigatus mycoviruses.Mycoviruses are usually detected by isolation of genomic dsRNA or a dsRNA replicative intermediate. Mycovirus RNAs are further characterized by purification from agarose gels and sequencing of cDNAs generated from de dsRNA fragments . Deep seA. fumigatus strains  for 30 sec at 7,000 rpm. Total RNA was isolated with TRIzol reagent according to the protocol of the supplier (Life Technologies).The mRNA library was constructed using the Illumina TruSeq RNA Sample Preparation v2 Guide  according to manufacturer’s instructions. The Low Sample (LS) protocol was used. Briefly, each RNA sample (1 μg) was enriched using oligo(dT)-tagged beads. RNA samples were fragmented into smaller pieces and used to synthesize cDNA. The library construction involved end repair, A-tailing, adapter ligation, and amplification. Mean length of each library was approximately 260 base pairs (bp). Sequencing was performed in a paired-end 2 x 75 bp mode on a NextSeq500 sequencer .A. fumigatus Af293 CADRE 30 using CLC Genomics Workbench version (11.0). Unmapped reads were collected and used for de novo assembly using CLC Genomics Workbench version (11.0) with default settings.Illumina reads were mapped to the genome sequence of De novo derived contigs were analyzed in a blastx search using the mycovirus protein database.Standalone Blast version 2.6.0 (Reference) was used to generate a Blast database containing all known mycovirus protein sequences. Mycovirus protein sequences were extracted from GenBank files and described in For phylogenetic analysis, the RNA dependent RNA polymerase (RdRp) encoding regions of the contigs and the reference mycovirus sequences were aligned using CLC Genomics Workbench version 11.0 and maximum likelihood trees were constructed with 1000 bootstrap replicates.A. fumigatus strains (CYP51A mutations leading to azole resistance in strains 8 and 10. Isogenic isolates from patient 2 (strains 2 and 5) did not show changes in the CYP51A gene although strain 5 was azole resistant in contrast to strain 2. Six unrelated strains were isolated from the environment. Three of these strains  contained mutations in the CYP51A gene resulting in azole resistance. The other three strain  were susceptible to azole treatment. Sequencing reads were mapped against the genomic DNA sequence of A. fumigatus Af293 reference strain. Unmapped reads were collected and used for de novo assembly. Resulting contigs were analyzed with a standalone version of the Blastx program against a home-made database generated from known mycovirus protein sequences . Nucleotide sequences of both segments are available in GenBank under accession numbers MH192991 and MH192992.Phylogenetic analysis was performed using RdRp protein sequences from alpha-, beta-, gamma-, and delta partitiviruses demonstrating a clustering of the newly found Aspergillus fumigatus partitivirus with Alternaria alternata partitivirus-1 and Botryosphaeria dothidea partitivirus-1 . The thrFrom two out of 11 strains, multiple contigs were generated representing virus genomic segments of the previously described Aspergillus fumigatus polymycovirus-1 (AfuPmV-1) . All fouThe sequences of the newly found AfuPmV-1 variant are available in GenBank under accession numbers MH192993, MH192994, MH192995, and MH192996.A. fumigatus strains were found positive for novel narnaviruses  demonstrated an amino acid sequence similarity of approximately 35% with Beihai narna-like virus-21. However, these segments appeared to be incomplete (data not shown).Contigs generated from seven aviruses . A contiApart from the Aspergillus fumigatus narnavirus segment, an extra contig was generated from strain 3 reads. The 2500 bp fragment contained one single ORF encoding mitovirus RdRp. Blast analysis revealed that the protein had an amino acid sequence similarity of 31% with Sclerotina sclerotiorum ourmia-like virus-2. The genomic organization of the viral segments with complete protein coding regions are depicted in A. fumigatus narnaviruses and mitovirus , Aspergillus fumigatus narnavirus-2 (AfuNV-2), and Aspergillus fumigatus mitovirus-1 (AfuMV-1). Nucleotide sequences of the mitovirus and two narnaviruses are available in GenBank under accession numbers MH192988, MH192989, and MH192990, respectively.Phylogenetic analysis was performed using RdRp sequences from plant and fungal narna- and mitoviruses and the newly found itovirus . The anaA. fumigatus for the detection of RNA mycoviruses. The last few decades, new molecular methods gained more insight into the composition of the virosphere including mycoviruses. The number of RNA mycoviruses found in A. fumigatus so far, was limited to partitivirus, chrysovirus, and polymycovirus [Narnaviridae sequences from seven out of 11 strains. In total, nine out of 11 strains (81%) tested were infected with one or more mycovirus species. This is higher than reported by others [Aspergillus species for the presence of mycoviruses (reviewed by Kotta-Loizou and Coutts) [A. fumigatus strains. Bhatti et al. found dsRNA segments in 6.6% of 366 A. fumigatus strains tested. Only 5 (1.4%) of these strain were infected with chrysovirus [Determination of the transcriptome results in vast amounts of RNA sequences from the cells of interest. Nearly all sequencing reads will be generated from cellular RNAs. However, the RNA content of a cell also includes parasitic molecules like RNA virus genomes and their replicative intermediates. In this paper, we describe the use of sequencing reads from transcriptomic experiments in ycovirus , 13. They others , 15. Sev Coutts) . The perysovirus . The numysovirus . This deAspergillus [Gammapartitivirus genus which includes many fungal specific partitiviruses. In this study, we found a new partitivirus, Aspergillus fumigatus partitivirus-2 (AfuPV-2). Phylogenetic analysis showed that AfuPV-2 is related to Alternaria alternata partitivirus-1 and Botryosphaeria dothidea partitivirus-1 [Gammapartitivirus genus but the clade might be considered as an intermediate between the Gammapartitivirus and Deltapartitivirus genera. Xavier et al. described genus specific sequences in the 5’-UTR of partitiviruses [Gammapartitivirus specific element is found in Aspergillus fumigatus partitivirus-2 5’-UTR as well  has been detected in a single occasion . This vie family . In thisNarnaviridae) are not encapsidated [Narnaviridae family includes the Narnavirus and Mitovirus genera. Mitoviruses are thought to reside exclusively in the mitochondria, whereas Narnaviruses can replicate in the cytoplasm of the infected host [Narnaviridae forms infectious agents replicating in plants and fungi. Until now, no Narnaviridae were detected in Aspergillus. Here, we report the nucleotide sequences of two Aspergillus fumigatus narnaviruses and one mitovirus which are related to known fungal Narnaviridae.Members of the Narnaviridae family belong to a group of viruses without a coat protein encoding gene. Consequently, the naked RNA viruses  [p to 90% , 22. Duep to 90% –27. HoweThe number of viruses infecting filamentous fungi seems to be underestimated so far. Until now, only one single stranded DNA mycovirus was detected . No doubS1 Table(PDF)Click here for additional data file.


The perception of choice in the caregiving role has emerged as a key theme in qualitative gerontological caregiving research but few studies have examined choice quantitatively. The aim of our study was to test whether perceived choice moderated the relation between level and duration of care and the health impact of caring for a parent over the age of 65. We tested these questions in a series of structural equation models using existing data from the National Alliance for Caregiving, Caregiving in the U.S. 2015 Survey. We found that for adult-child caregivers of parents who reported a lack of choice in taking on the caregiving role, greater responsibilities for assistance with activities of daily living (ADLs) (p<0.01) and instrumental ADLS (p<0.01), and greater time providing care (p<0.05) predicted the negative impact of caregiving on caregiver health. The number of ADLs performed also predicted the emotional stress of caregiving for parents (p<0.01). Conversely, for caregivers who reported that they did have a choice in taking on the caregiving role, level and duration of care were not significantly related to the impact of caregiving on caregiver health, or the emotional stress of caregiving. Women were significantly more likely to report a lack of choice than men (p<0.05). Additional research is needed to explore the meaning of choice, and the ways in which choice may be especially constrained for daughters who care for older adults, in order to develop interventions to ameliorate the potentially deleterious health effects of caregiving on adult-children.


RNAbound, for finding the boundaries that are based on probabilities of evolutionarily conserved base pairings. We tested the performance of two different methods on a limited number of Rfam families using the annotated structured RNA regions in the human genome and their multiple sequence alignments created from 14 species. The results show that multiple sequence alignments improve the boundary prediction for branched structures compared to single sequences independent of the chosen method. The actual performance of the two methods differs on single hairpin structures and branched structures. For the RNA families with branched structures, including transfer RNA (tRNA) and small nucleolar RNAs (snoRNAs), RNAbound improves the boundary predictions using multiple sequence alignments to median differences of −6 and −11.5 nucleotides (nts) for left and right boundary, respectively (window size of 200 nts).Self-contained structured domains of RNA sequences have often distinct molecular functions. Determining the boundaries of structured domains of a non-coding RNA (ncRNA) is needed for many ncRNA gene finder programs that predict RNA secondary structures in aligned genomes because these methods do not necessarily provide precise information about the boundaries or the location of the RNA structure inside the predicted ncRNA. Even without having a structure prediction, it is of interest to search for structured domains, such as for finding common RNA motifs in RNA-protein binding assays. The precise definition of the boundaries are essential for downstream analyses such as RNA structure modelling, e.g., through covariance models, and RNA structure clustering for the search of common motifs. Such efforts have so far been focused on single sequences, thus here we present a comparison for boundary definition between single sequence and multiple sequence alignments. We also present a novel approach, named  Furthermore, long non-coding RNAs (lncRNAs) can contain local functional structures, e.g., lncRNA GAS5 forms a secondary structure that binds the glucocorticoid receptor (GR), which in turn prevents GR from binding to the glucocorticoid-response elements contained in responsive genes [The function of RNA is often guided by its structural conformation, which is in turn determined by its sequence composition. The classic examples of structured non-protein coding RNAs (ncRNA) include transfer RNAs (tRNA), ribosomal RNAs (rRNA), and precursor microRNA (pre-miRNA). In addition, untranslated and intronic regions of the messenger RNAs (mRNA) often contain ve genes . MoreoveRNAz predictions in Defining the RNA structure domains has been addressed at the single sequence level, first explicitly by Dotu et al. . They deorphisms  and furtorphisms . For exaorphisms . The usecis-regulatory elements are evolutionarily conserved independently of their sequence conservation. Thus, many RNA folding methods utilize the conservation signal, such as compensating base pair changes, for predicting conserved RNA secondary structures and structured ncRNAs from a set of aligned or un-aligned multiple sequences. We recently showed that there is indeed an effect for clustering RNA structures, e.g., from the same RNA family, when going from single sequences to multiple structure-based RNA alignments containing covariating base pairs [RNAbound, extends on the fitness function of Dotu et al. by also considering base pairing probabilities that span over the structure boundaries. Base pairing probabilities are calculated with PETfold [The structures of most ncRNAs and se pairs . Here, w PETfold  whose mo PETfold .CMfinder [Evofold [RNAz [CMfinder) or slice the genomic sequence into overlapping windows and search for structured ncRNAs within each window individually. Although windowing helps to reduce the computation time, the fixed window size may be larger or shorter than the actual length of the structured RNAs. For this reason, the further analysis of these data requires highly accurate prediction of RNA structure boundaries.Genomic screens for ncRNAs predict thousands of potential genomic loci of structured RNAs, such as CMfinder , Evofold[Evofold  and RNAzld [RNAz ,13 (revild [RNAz ). Since RNAbound, a new software that implements a novel fitness function for searching boundaries of structured RNA regions from the base pairing probability matrix.Here, we investigate into putative improvements of structure boundary predictions when traversing from single sequences to multiple sequence alignments. We also present self-contained base pairings inside of the larger sequence f is the length of flanking sequences up- and downstream of segment f. The three components can be described by scores based on base pairing probabilities: (1) score m and n. To avoid our boundary estimates from being mostly driven by a small number of high base bairing probabilities, we replace the base pairing probabilities In this section, we introduce a new fitness function to find the segment ocARNA-P , is defiFinally, the position of the segment detected with maximum score RNAbound, accepts as input a single sequence or a multiple sequence alignment. The base pair probabilities RNAfold v2.4.1 [−p and other default parameters), and for a multiple alignment with PETfold v2.1 [−r and other default parameters).Our implementation, named d v2.4.1  (with −pold v2.1  (with −rn = 3103) by using the known structured RNA sequences from various resources such as Rfam (v12.2) [BLAST [multiz (tab) [We defined a set of annotated structured regions ( (v12.2) , mirBase (v12.2) , tRNAdb  (v12.2) , rRNA (s (v12.2)  and snoR (v12.2) ; and map) [BLAST  tool to ) [BLAST  based oniz (tab)  to form Furthermore, we created windows of sizes 100, 150, and 200 nts that are centered around the middle position of the structured RNA annotations and performed the following quality filters: (a) the selected window size of an alignment should cover the start and end of the mapped known RNA sequence plus flanking sequences of 10 nts, (b) no overlapping annotations within 200 nts upstream or downstream, (c) the number of bases involved in the base pairing (of the annotated secondary structure obtained from Rfam) should be greater than or equal to 20%, (d) sequences with gaps more than 75% of the length of the alignment are removed from the alignment, (e) after gap filtering the alignment should have minimum three sequences, and (f) the mean pairwise sequence identity of the alignment should be ≥60% and <95%. The numbers of filtered structured RNA annotations in each step are listed in RNAshape [Under each Rfam family, we further classified the structures into two main groups: single hairpin structure and branched structures based on the abstract shape (predicted using RNAshape ) of the RNAbound, we used the benchmark dataset of multiple sequence alignments of the window sizes 100, 150, and 200 nts centred around structured RNA regions  from each of the alignments under the different window sizes. The parameters of the fitness function boundary difference between the RNAbound predicted boundaries  across all Rfam families  on the single sequence is 0.0 and −2.0 (−2.0 and −4.0) for left and right boundary, and on the multiple sequence alignment it is 4.0 and 0.0 (1.0 and −1.0). In the case of RNAs that form branched structures , we observed similar results with the median value of boundary differences of −5.0 and −7.0 (1.0 and −2.0) for left and right boundary on single sequence, and −5.0 and −9.0 (0.0 and −2.0) on the multiple sequence alignment  and multiple sequence alignment (−14 and −9). However, the analysis specific to each Rfam family shows that the impact of multiple sequence alignments for the boundary prediction varies across different Rfam families  for all these cases, we noticed that, in the case of the single sequence, RNAfold predicts either one long hairpin structure or bifurcated structures with a small stem between 5’ and 3’ ends. In contrast, PETfold predicts two independent stem-loop structures from the multiple alignments. In some cases, both RNAbound and Dotu et al. failed to cover one of these two stem-loop structures within the predicted boundaries, and thus the performance is lower compared to the prediction from a single sequence.In the larger window sizes 150 and 200, the boundaries predicted using the single sequence are in excess compared to the multiple sequence alignment for branched RNA structures see b,c. Thisfamilies . In the proaches . In otheproaches . In the RNAbound and Dotu et al. approaches, but not for single hairpin structures. This improvement is significant with RNAbound for window sizes 150 and 200, and with Dotu et al. for window sizes 100 and 150  as compared to the Dotu et al. approach (with median differences of −18 and −86). After going through the individual cases of snoRNA results, we noticed that, in some cases, the Dotu et al. approach failed to cover the full snoRNA structure that comprises two hairpins adjacent to each other. RNAbound predicted boundaries are in excess, 22 bases on the left and eight bases on the right, as compared to the actual boundaries. In contrast, the Dotu et al. approach predicted short of boundaries, −16 and −93 respectively, on the left and right boundaries. In other cases, RNAbound predicted boundaries that are in short of the actual boundaries; however, the boundaries predicted are still closer to the actual boundaries when compared to the Dotu et al. predictions. An example of this is shown in the The Rfam families that form branched RNA structures show high variability in the boundary prediction for both proaches . InteresRNAbound and Dotu et al. approaches are comparable and their predicted boundary differences vary with respect to the RNA families that form either single hairpin or branched secondary structures. Overall, we found that RNAbound predicts boundaries in excess for single hairpin structures, and short for branched structures when compared to actual boundaries on the larger window sizes 150 and 200 on the multiple sequence alignment. Whereas the Dotu et al. approach performs better for miRNAs and single hairpin cis-regulatory structures, RNAbound is in general superior for branched structures on multiple sequence alignments. This may be due to the fact that the weight combination . In f. Alternative approaches such as machine learning may further improve the boundary prediction accuracy from a global perspective when being trained by a large data set of different RNA families.In this study, we have compared two fitness functions that are based on base pairing probabilities. Despite the similarity of the fitness functions, they outperform each other on different RNA structure families. Our method, In our benchmark dataset, the boundaries of single hairpin structures are more precisely predicted from single sequences than from multiple alignments . HoweverRNAbound can comprise more than one self-contained structure. In the benchmark data set, the initial 2385 multiple sequence alignments that overlap at least one annotation originating from an Rfam seed alignment contain only around 5% that overlap (≥50 nts) with more than one annotation . Because the majority of the annotated structures are not close to another structure, we focused on the boundaries of isolated structures to test the fitness functions. In future work, not only the self-contained RNA structure of the highest score should be searched, but, instead, all non-overlapping structures of comparable scores to identify clusters. For the study sub-optimal self-contained structures, we provide a script that filters non-overlapping regions with highest fitness function scores from the RNAbound output.Self-contained RNA structures can occur in clusters including some small RNAs, such as tRNAs and microRNAs. Hence, the input sequence or alignment to RNAbound are in post-processing the recurrent RNA structures detected through de novo structured RNA finding approaches. Either shrinkage of boundaries of window-based approaches or expanding the boundaries of local alignment based approaches may better capture the structural features of RNAs. In addition, identifying and annotating the genomic occurrence of homologous RNA structure motifs from sets of biologically related sequences will improve our understanding of the structure-function relationship of RNAs and the molecular mechanisms underlying their regulatory features. Putative RNA structure motifs may be hidden in signals from RNA sequencing experiments that measure how RNAs interact with other molecules, such as cross-linked RNA immuno-precipitation for exploring RNA-protein interactions. Post-processing comprises RNA structure modelling, e.g., through covariance models [DotAligner [Putative applications of e models , and clutAligner  searches


Non-Coding RNA Scientific Board wishes you an exciting and fruitful read.We are delighted to share with you our sixth Journal Club and highlight some of the most interesting papers published recently. We hope to keep you up-to-date with non-coding RNA research works that are outside your study area. The Science, Qiang Cai and coworkers showed that extracellular vesicles are involved in the process [Although it is known that hosts can send small RNAs (sRNAs), to pathogens, to inhibit their virulence, the mechanism of this transfer is unclear. In a recent issue of  process .Botrytis cinerea) cells from infected Arabidopsis tissues and identified 42 host-originated sRNAs . In animals, microRNAs are transferred via exosomes, which are derived from multivesicular bodies (MVBs). Tetraspanins (TET) are exosome markers in mammals, and Arabidopsis TET8 accumulates at the infection sites. At these sites, MVBs fuse with the plasma membrane to release vesicles , further supporting the notion that plants secrete exosomes to deliver sRNAs to their pathogens. Half of the transferred sRNAs have predicted targets, with a bias towards vesicle-trafficking pathways . The target genes are down-regulated after an infection, unless the plant host is deficient in trans-acting small interfering RNA (tasiRNA) production. The cleavage of mRNAs could be detected in the fungus, suggesting that the transferred sRNAs silence the target genes through a transcript destruction.In this study, the authors purified fungal .SUMO1-targeting miR, in failing human and mouse hearts. In a model of pressure overload, overexpression of miR-146a reduced SUMO1 expression, whereas miR-146a inhibition normalized SUMO1 expression and improved the cardiac function. Intriguingly, the authors demonstrated that miR-146a was not directly produced by cardiomyocytes, but it was first synthesized by activated fibroblasts (myofibroblasts), following an injury, and then transferred via extracellular vesicles to cardiomyocytes. This discovery has major implications in the clinical scenario, since targeting miR-146a might provide a novel therapeutic approach for the treatment of HF.The expression of the small ubiquitin-like modifier 1 (SUMO1) has been reported to be significantly reduced in both human and murine heart failure (HF). However, the molecular mechanisms underlying SUMO1 upregulation had not been investigated, hitherto. Changwon Kho and colleagues identified a specific microRNA (miR) as a key factor in the pathophysiology of HF . The autGenes & Development, Chuan Huang and co-workers illuminated a size-dependent mechanism of nuclear transport for circRNAs [While circular RNAs are co-transcriptionally synthesized in the nucleus, they are almost exclusively sequestered into the cytoplasm. This suggests circular RNAs (circRNAs), like other RNAs, are actively exported from the nucleus. In a recent issue of circRNAs .Drosophila, the authors identified an RNA helicase—Hel25E—which caused nuclear retention of nascent circRNAs, longer than 800 nt. However, depletion of the principal mRNA nuclear export factor, the NXF1:NXT1 complex, had no quantifiable effect on circRNA transport. Extrapolating these results to human cells, targeting the two Hel25E homologs in HeLa cells, URH49 (DDX39A), and UAP56 (DDX39B), resulted in a nuclear retention of short (<356 nt) and long (>1298 nt) circRNAs, respectively.Using a targeted, small-interfering RNA (siRNA) screen against proteins involved in nuclear RNA export in This study identified the catalytic ATPase and helicase domains, of these human RNA helicases, as critical factors in defining circRNA export-size preference. Future studies could illuminate factors contributing to the nuclear export of intermediate-sized circRNAs and the possible involvement of distinct nucleoporins within the nuclear pore complex.PNUTS (Phosphatase 1 Nuclear Targeting Subunit) was originally identified as a modulator of the PP1 phosphatase on Retinoblastoma protein (Rb). PNUTS is up-regulated in cancer cells where it acts as a potential oncogene through sequestration of the tumor-suppressor PTEN in an inactive state, in the nucleus.Grelet and colleagues identified an alternatively spliced isoform of PNUTS mRNA generated through an alternative acceptor splice site, in exon 12. The resulting frame-shift disrupted the translation capacity of the transcript, hence producing a long non-coding RNA (lncRNA-PNUTS) . The autThis study reported an example of a mammalian lncRNA—produced by an originally protein-coding gene—through alternative splicing, which is involved in the early stages of EMT and possibly cancer progression.Forward genetic screens have yielded fantastic insights into gene-phenotype relationships, for decades. Bester and colleagues have adopted this strategy to identify long non-coding RNAs (lncRNAs), as well as protein-coding genes, which, when over-expressed, confer altered sensitivity to Ara-C, a deoxycytidine analog used to treat acute myeloid leukemia . As lncRAXL. Validating this strategy, a higher expression of GAS6-AS2, as well as AC008073.2, were associated with poor prognosis and decreased disease-free survival, in acute myeloid leukemia (AML) patients treated with Ara-C. This approach showed the power of forward genetic approaches to capture key regulators among lncRNA-encoding genes.The authors identified hundreds of candidate lncRNAs, either associated with Ara-C sensitization, or resistance. They further confirmed the role of several lncRNA, including the GAS6 antisense transcript (GAS6-AS2). GAS6-AS2 was further shown to act in cis mechanisms to upregulate the expression of its cognate protein-coding gene (GAS6), and in trans mechanisms to alter the DNA methylation at We have a deep interest in modeling RNA–protein , RNA–RNAuc.339) to explain how uc.339 promotes carcinogenesis [The transcribed ultraconserved regions (T-UCRs) had been proven to play an important role in human carcinogenesis, but there is still a large blank in the mechanisms and the consequences of their expression dysregulation in cancers. Recently, our colleagues revealed a new pathway of the transcribed ultraconserved region 339  database and found that, in 210 non-small cell lung cancer (NSCLC) patients, the high expression of


Recent studies have revealed that non-coding regions comprise the vast majority of the human genome and long non-coding RNAs (lncRNAs) are a diverse class of non-coding RNAs that has been implicated in a variety of biological processes. Abnormal expression of lncRNAs has also been linked to different human diseases including cancers, yet the regulatory mechanisms and functional effects of lncRNAs are still ambiguous, and the molecular details also need to be confirmed. Unlike protein-coding gene, it is much more challenging to unravel the roles of lncRNAs owing to their unique and complex features such as functional diversity and low conservation among species, which greatly hamper their experimental characterization. In this review, we summarize and discuss both conventional and advanced approaches for the identification and functional characterization of lncRNAs related to hematological malignancies. In particular, the utility and advancement of clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system as gene-editing tools are envisioned to facilitate the molecular dissection of lncRNAs via different knock-in/out strategies. Besides experimental considerations specific to lncRNAs, the roles of lncRNAs in the pathogenesis and progression of leukemia are also highlighted in the review. We expect that these insights may ultimately lead to clinical applications including development of biomarkers and novel therapeutic approaches targeting lncRNAs. Advanced genome- and transcriptome-wide analyses have consistently illustrated that only 2% of the human genome encodes proteins while > 75% is actively transcribed into non-protein-coding RNAs , 2. ThisLncRNAs form a newly emerging class of ncRNAs with multifunctional competences. They are typically transcribed by RNA polymerase II  and do nRecent studies have demonstrated diverse roles of lncRNAs in the regulation of gene expression at epigenetic, transcriptional, and post-transcriptional levels via different mechanisms , 17–19. To date, over 4000 studies have shown the biological significance of lncRNAs in cancer development and progression. The molecular mechanisms linking them to specific biological functions and cellular phenotypes are now beginning to be realized. In this review, we highlight and reinforce the fundamental approaches recently used for functional characterization of lncRNAs. The definition of “functional characterization” is set at a higher level than gene regulation, from in vitro cellular effects to clinical association studies; especially, we target the more recent studies in cancer research and further narrow down to hematological malignancies. Although there were extensive works on identification and characterization of lncRNAs in different types of diseases, the overall knowledge and understanding of lncRNAs remain largely unclear especially for those involved in blood malignancy , 23. Man+) RNA and is only able to detect highly expressed transcripts. Thus, a number of effective methods have been further developed for detecting and characterizing lncRNAs.Tissue specificity and low expression level are generally considered as challenging features influencing the identification of novel lncRNAs from whole transcriptome. Thus, it is necessary to select appropriate tools based on sample specificity for identifying lncRNAs. Various advanced methods are commonly used to identify lncRNAs: serial analysis of gene expression (SAGE), tiling array, cap analysis of gene expression (CAGE), and high-throughput RNA-sequencing (RNA-seq). All of these techniques mainly depend on the discovery of transcripts from non-protein-coding genomic regions, which are devoid of ORFs. Initially, conventional microarray was utilized to analyze only protein-coding mRNAs; such technique mainly probes the expression of polyadenylated (poly(A)SAGE is the first high-throughput sequencing method of analyzing transcriptomes. SAGE can comprehensively profile gene expression by generating short-stretches of unbiased cDNA sequence called SAGE tags via restriction enzyme digestion , 26. In Tiling arrays were originally used for both identification and characterization of lncRNAs. Briefly, it allows the global transcription mapping of genomic regions in order to provide comprehensive alternative splicing analysis, and discovery of novel transcriptional sites and polymorphisms –30. ThisCAGE is one of the most advanced techniques used in molecular genomics. Generally, this NGS-based method allows generating a snapshot of the 5′ ends of mRNAs . CAGE ma+ and poly(A)− RNA fractions should be targeted in order to identify all types of lncRNA . Th. Th52]. Nevertheless, a study reported that inhibiting lncRNA MALAT1 expression was solely achieved by deleting its major promotor, but not the annotated upstream promoter . This imAs a general caution, excision of a genomic DNA sequence may have unexpected impact on the expression of neighboring genes. Bidirectional transcription is an example that transcription from both DNA strands can be suppressed by deleting the shared promoter segment, which may confound the interpretation of an observed phenotype. Therefore, it is believed that targeting strand-specific proximal TSS element like DNaseI/H3K4me3 hallmark could minimize those non-specific effects produced by deletion of the entire promoter.In addition to knockout approaches by excision of genomic regions, nuclease cleavage technology can be further utilized to achieve lncRNA knockdown by knocking in destabilizing elements or transcriptional stop signal into the gene  of interest Fig.  62, 63], 6362, 6transcript of lncRNA Haunt and its genomic locus in the regulation of HOXA gene clusters . Su. Su64]. To conclude, CRISPRi/a allows down- or upregulation of lncRNA expression in a highly specific manner without the requisite for direct genomic editing and changes of DNA sequence. Meanwhile, such epigenetic silencing of gene expression, like genomic region deletion, acts at the DNA level so that it could target lncRNAs without competing with endogenous RNA machinery when compared to RNAi methods. Nonetheless, an effective use of CRISPRi/a methods requires prerequisites like gathering information about the location of enhancer or promotor elements, and confirming that these regulatory elements exclusively control the target lncRNA transcription, but not sharing function with other genes.After all considerations, the major limitation of using CRISPR-Cas9 to target lncRNAs is the complex architecture of genomic loci surrounding different lncRNA genes, which may highly increase the possibility of disturbing neighboring or overlapping genes. This may greatly reduce the specificity of genetic manipulation of lncRNAs and lead to a false positive phenotypic change resulting from the intervention of neighboring genes. Actually, a genome-wide analysis revealed that only about 38% of 15,929 identified lncRNA loci could be safely and specifically targeted by CRISPR-Cas9-based manipulation , whereas two-thirds of lncRNA loci would be at risk of affecting neighboring genes unintentionally . With coRNase activity instead of DNase activity in Cas9, and hence it can be harnessed for robust degradation of single-stranded RNA transcripts , RAP-RNA[FA], and RAP-RNA[FA-DSG]  generates intermolecular RNA-RNA uridine-based crosslinks via 4′-aminomethyltrioxalen (AMT), a crosslinker that does not react with proteins. To detect both direct and indirect RNA-RNA interactions, RAP-RNA[FA] uses formaldehyde (FA) to crosslink proteins to RNAs or other proteins. Finally, to detect indirect RNA-RNA interactions, RAP-RNA[FA-DSG] combines both FA and disuccinimidyl glutarate (DSG) to efficiently crosslink proteins in multiple-protein complexes in order to capture RNAs indirectly linked via such protein complexes. This combined approach was first used to unveil the direct interaction between U1 snRNA and pre-mRNAs, as well as indirect interaction of lncRNA-MALAT1 through protein intermediates [RNA antisense purification followed by RNA sequencing (RAP-RNA) is a modified RAP method commonly used to analyze the RNA-RNA interaction by utilizing different cross-linking methods. There are three subtypes of RAP-RNA: RAP-RNA[FA-DSG] . To detemediates .Cross-linking, ligation, and sequencing of hybrids (CLASH) is a powerful technique for analyzing RNA-RNA interaction by UV cross-linking. It has several advantages over protein-protein cross-linked cells because of using UV light. In brief, immediately after purifying UV-cross-linked RNA-protein complexes, RNA molecules are ligated together in order to generate chimeric RNAs and finally high-throughput sequencing is performed. Furthermore, this advanced method can also identify miRNA-mRNA interaction mediated by human argonaute protein and discover novel snoRNA-rRNA interactions in yeast .Functional characteristics of lncRNAs can be predicted by detecting their subcellular localization by means of fluoresence in situ hybridization (FISH). RNA single-molecule FISH (smFISH) is used to find the subcellular localization of lncRNAs and can suggest possible functional role of selected lncRNAs  . HoweverGenetic manipulation approaches targeting lncRNAs), certain cellular effects should be examined in order to investigate the functional output of the target lncRNA. Here, we discuss several crucial biological functions: cell proliferation, cell cycle regulation, hematopoiesis, drug resistance, and angiogenesis, which are critical physiological processes contributing to the pathogenesis or progression of blood malignancies upon deregulation. Below, we illustrate lncRNAs associated with these cellular functions, particularly those that have been implicated in hematological diseases, and some typical approaches used to study them.As lncRNAs play an important regulatory role in normal development and functioning, their deregulation may result in a pathological condition. Increasing evidence has demonstrated that multifunctional lncRNAs are associated with different complex disorders including hematological malignancies. In this section, we focus on the biological implications and/or functional outputs of lncRNAs rather than their mechanistic functions. Experimentally, after in vitro genetic manipulation has been carried out . After the incubation period, absorbance is measured by simple spectrophotometry and finally data analysis carried out.Most recently, several approaches have been commonly used to characterize lncRNA involvement in hematopoietic cell proliferation Table . siRNA/sINK4b-ARF-INK4a locus encoding three important cyclin-dependent kinase (CDK) inhibitors  [in trans by recruiting polycomb repressive complexes PRC1 and PRC2 to the INK4b-ARF-INK4a locus, and this results in the silencing of the gene cluster by H3K27-trimethylation [CDKN2A/B that encodes CDK inhibitors.Functionally related to cell proliferation, numerous lncRNAs have been reported to participate in cell cycle regulation through their diverse roles as regulators at epigenetic, transcriptional, and post-transcriptional levels . A well-16INK4a) . It has hylation . A detaiCCND1 [Another example of cell cycle-associated lncRNA is MALAT1, which is related to numerous human cancers and whose activity has been implicated in acute monocytic leukemia  and multCCND1 , and PANCCND1 , 112 areThere are three widely used approaches for analyzing cell cycle progression by flow cytometry . The firA major cause of blood malignancies is the dysregulation of hematopoiesis, which is a complex and highly ordered process requiring tight coordination of cell lineage specification and differentiation under the precise control of specific gene expression. Emerging discoveries implicated the involvement of lncRNAs in both normal and malignant hematopoiesis mainly by their influence on the expression of key regulators at particular differentiation stage. Table HOXA genes [HOXA1 and HOXA4 expression during retinoic acid-induced myeloid differentiation and hence attenuated the transcription of myeloid differentiation genes such as CD11b and CD18 [For instance, HOTAIRM1 is a myelopoiesis-associated regulatory lncRNA identified by tiling array and is transcribed antisense to the XA genes . HOTAIRMand CD18 . In addiand CD18 , 116, lnand CD18 –119, andand CD18 . PU.1 ASand CD18 .+ human hematopoietic stem and progenitor cells (HSPCs) by shRNAs, which then resulted in defective granulocytic differentiation in vitro. The cells were examined by leukocyte peroxidase staining and cell surface marker CD66b. In addition, methylcellulose-based and collagen-based colony-forming assays were further carried out, and the results revealed a reduction of myeloid colony formation after suppression of LINC00173, whereas erythroid colony formation was unaffected. The findings suggested that LINC00173 was not only involved in granulocytic differentiation but also in the development of early myeloid progenitor cells [The colony-forming cell (CFC) assay is a technique utilized to assess the proliferation and differentiation ability of hematopoietic progenitors by their ability to form colonies in a semi-solid medium. When cultured with appropriate cytokines, CFCs can divide and differentiate into a colony of more mature cells, which can in turn be identified by light microscopy. The morphology and number of colonies generated by a fixed number of input cells can provide preliminary information about the capability of progenitor cells to grow and differentiate. However, this assay is useful for assessing myeloid , but not lymphoid, differentiation. A study employing the use of CFC assay uncovered the function of LINC00173, which showed a very specific expression in mature granulocytes . By bioior cells .Despite the availability of therapeutics for treating hematological malignancies, development of drug resistance is still a stumbling block for effective treatment. For example, imatinib, a selective BCR-ABL1 kinase inhibitor, is highly effective in treating CML. Yet, some patients still develop imatinib resistance and the underlying mechanism of imatinib resistance is still far from complete. Mechanistic studies have identified that imatinib resistance acquired in CML can result from point mutations in the BCR-ABL kinase domain , overexpIt has been shown that overexpressed lncRNA UCA1 modulates CML-imatinib resistance by acting as a competitive endogenous RNA (ceRNA) against miR-16, and this in turn stimulates MDR1 expression and alters the drug efflux system . BesidesTo investigate the correlation between lncRNAs and drug resistance, different approaches are commonly used as described below. The prerequisite of functionally characterizing drug resistance-associated lncRNAs is to generate in vitro cell line resistant to anti-cancer drugs. This is done by continually treating the cells with increasing doses of an anti-cancer drug for a certain period of time , 127. InAngiogenesis is the process of generating new blood vessels from a pre-existing vascular network. In normal physiological condition, angiogenesis is strongly maintained by a precise balance between stimulatory and inhibitory signals –132. HowAdvanced research has identified the functional involvement of exosomes in CML-mediated angiogenesis. Briefly, exosomes secreted by CML cells contain different functional molecules such as miR-92a, a member of miR-17-92 cluster (angiogenesis stimulator), which can be internalized into human umbilical vein endothelial cells (HUVECs) and modulates their tube formation by activating Src signaling pathway , 139. Mosolid cancers as listed in Table The significance of angiogenesis in blood cancers, such as leukemia, has been noticed since it plays an important role in both hematopoiesis and leukemogenesis . SeveralLncRNAs are emerging as crucial players in different pathological conditions and there have been many studies showing their dysregulation associated with human diseases –159. SucLncRNAs can potentially be attractive biological markers for diagnostic and prognostic application because their expression patterns are much more tissue-specific or disease type-specific when compared with those of protein-coding genes . Such spProstate cancer antigen 3 (PCA3) is the best-known example of lncRNA applied in clinical diagnosis since it demonstrated higher diagnostic performance than the conventional detection of prostate-specific antigen (PSA) serum level. PCA3 is a prostate-specific lncRNA and is overexpressed in > 90% of primary prostate tumors compared to benign tissues , which cIndeed, lncRNAs also provide diagnostic and prognostic value in hematological diseases. A clinical research study revealed a subset of lncRNAs called B-ALL-associated long RNAs (BALR) by microarray analysis. It has been found that BALR are able to predict the cytogenetic subtypes of B-acute lymphoblastic leukemia (B-ALL) among the most prevalent abnormalities: mixed lineage leukemia (MLL) rearrangement, E2A-PBX1 translocation, and TEL-AML1 fusion. Upon further clinicopathologic data analysis, a high expression level of BALR-2 was found to be associated with poor survival and reduced responses to prednisolone used in treatment of B-ALL . This fiAnother example is lncRNA PRAL, which was downregulated in primary multiple myeloma (MM) cells, especially in MM cells with del(17p). Analysis of survival curves revealed a significantly shorter disease-free survival and overall survival in MM patients with low PRAL expression . In the Overall, lncRNAs could conceivably serve as effective biomarkers for disease diagnosis and prognosis owing to their high specificity and relative ease of sampling. In addition, their differential expression can be quite readily detected by common molecular biology techniques such as microarray assay, real-time qPCR, and RNA-seq. It is expected that by combining different lncRNA candidates or together with conventional biomarkers, the clinical judgment of medical professionals can be greatly facilitated in the future. Nonetheless, PCA3 is the only lncRNA that has been recommended as an FDA-approved biomarker to date. The utility of lncRNAs as clinical biomarkers is still in its infancy and the possible use of most known disease-associated lncRNAs was not confirmed yet. Therefore, more studies investigating the precise relationship between lncRNAs and disease pathology are needed, and standardization of the detection approach is also required in order to promote the use of lncRNAs in clinical settings.On the other hand, the unique specificity of lncRNAs also makes them remarkable therapeutic targets by reducing off-target side effects. With the advances in oligonucleotide-based therapeutics, any particular member of human transcriptome, which cannot be targeted by small molecules or antibody-drugs, can be reached by nucleic acid-based drugs , 169. Inmouse mammary tumor virus)-PyMT breast cancer model, enhanced cell adhesion and cystic differentiation and reduced migration were shown upon ASO-targeted knockdown of MALAT1 [As a proof of concept, lncRNA MALAT1 as a therapeutic target has been tested in vivo in an animal model and has demonstrated therapeutic efficacy. In a mouse MMTV (f MALAT1 . Anotherf MALAT1 . AlthougLncRNAs have become a hot topic in the genome research field. We are entering a new era in which the correlations of lncRNAs with developmental process and diseases are being elucidated gradually. However, the functional characterization of lncRNAs is still in its infancy as this highly complicated layer of gene regulation is made possible by dynamic interaction with various molecules or complexes. Perturbation experiments, especially loss-of-function study, represent major tools to unravel the functional roles of lncRNAs. With the recent promising developments of different genome manipulation techniques, notably CRISPR-Cas systems, which have expanded its versatility from DNA to RNA level for targeting and manipulation, it is expected that these novel approaches will further evolve to become better customized for probing lncRNA function. Nevertheless, no single method can address all research questions. Therefore, researchers should carefully choose appropriate technologies that suit the purpose of study. By highlighting the approaches to identify and characterize lncRNAs involved in hematological malignancies, we hope that further understanding of lncRNA properties can be achieved and this finally helps to identify novel biomarkers and therapeutic strategies.


Shape-selective recognition of nucleic acid structures by supramolecular drugs offers the potential to treat disease. The Trans Activation Response (TAR) region is a region of high secondary structure within the human immunodeficiency virus-1 (HIV-1) RNA that complexes with the virus-encoded Transactivator protein (TAT) and regulates viral transcription. Herein, we explore different metallo-supramolecular triple stranded helicates (cylinders) that target the TAR bulge motif and inhibit the formation of TAR-TAT complexes and HIV infection. Cylinders that incorporate Ni(II) and Ru(II) showed the most potent anti-viral activity with limited evidence of cellular cytotoxicity. These metallo-supramolecular compounds provide an exciting avenue for developing a new class of anti-viral agents. Microrganisms, including diverse fungi, bacterial and viral species encode RNA species with distinctive 3-dimensional structures that play a role in their replicative life cycles and represent attractive targets for anti-microbial research3. However, RNA recognition by small molecule drugs is poorly developed compared to the large number of synthetic agents that target DNA motifs and proteins. The diversity and dynamic nature of RNA structures, reflecting its multiple functions, is a major challenge in designing RNA binding molecules, yet offers the potential for shape recognition of structural features of selected RNAs, providing a strategy in drug development and diagnostics5.Ribonucleic acid (RNA) is involved in a wide range of biological events, mediating the processing of genetic information from DNA to proteins, as structural components of many ribonucleoproteins and as non-coding elements with a variety of gene regulatory functions7. Current therapies for treating HIV infection target viral encoded enzymes that play an essential role in the replicative life cycle and include reverse transcriptase, protease and integrase8. Given the error prone nature of the reverse transcriptase coupled with a high replicative capacity, HIV exists as a quasispecies of viral variants that can develop resistance to anti-viral therapies. Efficient ART requires drug combinations targeting multiple enzymes in the viral life cycle. The highly conserved HIV TAR motif provides an attractive target for the design of small molecules that inhibit TAR-TAT complex formation; however, to date there has been a paucity of agents targeting this step in the viral life cycle.The retrovirus human immunodeficiency virus type 1 (HIV-1) encodes a Trans Activation Response (TAR) RNA element that comprises a conserved 59-nucleotide stem-loop structure located within the 5′ end of the transcribed viral mRNA Fig.  in the l9, although most of the examples reported to date focus on binding DNA; by comparison, the field of RNA recognition by metal complexes is unexplored10. There are only a few examples of metal-based-systems that target HIV TAR RNA16 or are conjugated to TAR binding organic compounds17. We previously reported a class of metallo-helicates23, also known as ‘cylinders’, that can bind DNA and RNA 3-way junctions22 and bulge structures23. We also reported these cylinders to have cytostatic and cytotoxic activity against mammalian cancer cell lines24, and bactericidal properties25. In particular, the iron cylinder (FeCy) comprising three strands of bis-pyridylimine ligands that coordinate two iron(II) atoms  cylinders for mammalian cancer cell lines24 and ability of iron(II) to promote HIV infection30, limits the potential application of this compound.The diverse 3D shapes of transition metal-based compounds, and their cationic charge make them ideal compounds to target specific nucleic acid structural motifsoms Fig. , was repIn this paper, we address these challenges and demonstrate that alternative metal containing supramolecular cylinders bind TAR RNA and reduce ADP-1 interaction(s) and importantly inhibit HIV replication in cell based systems with no significant toxicity.31  and ruthenium(II) triple stranded helicates26. We further confirmed equivalent binding properties of the three cylinders toward TAR RNA, by performing thermal stability and fluorescent intercalator displacement (FID) assays. Analysis of UV melting curves  of 12 °C. In FID assays, all three cylinders displace ethidium bromide from TAR RNA, with analogous similar displacement curves and DC50 values  nucleic acid targeting provides several advantages over more traditional approaches that target TAR RNA or TAT peptide. We have shown that such supramolecules inhibit HIV replication. Since the replication of many RNA and DNA viruses is dependent on secondary RNA structures36 we hypothesize that these cylinders will inhibit replication of a wide range of viruses. This opens intriguing new directions in the application of different supramolecular architectures to not only recognise different nucleic acid structure but to select their biological activity and address disease and infection.Many viruses replicate their genomes using viral encoded enzymes that lack proof reading activity and generate viral diversity that enables escape from anti-viral drugs and host immune responses. Genetic polymorphism has been reported in the HIV-1 TAR region resulting in variable bulge base sequences and/or small variations in the number of bases in the bulge37. Stock concentrations of compounds were prepared in sterile ultrapure water (from Sigma) for gel electrophoresis studies or in sterile PBS solution for cell biology experiments, by measuring absorbances with UV-Vis (Cary Varian 5000). TAR RNA sequence (5′-GGCCAGAUCUGAGCCUGGGAGCUCUCUGGCC-3′) was purchased from Integrated DNA Technologies (HPLC grade) and ADP-1 peptide (SFTTKALGISYGRKKRRQRRRPPQGSQTHQ-VSLSKQ) was purchased from Peptide Protein Research Ltd. (UK) with purity >98%. RNA and peptide concentrations of stock solutions were measured by UV absorbance employing the extinction coefficients provided by suppliers. All charts were prepared using SigmaPlot and statistical significance calculated using the ‘one way ANOVA’ analysis.Synthesis purification and characterisation of Fe, Ni and Ru cylinders were carried out according to previously reported procedures32P]ATP. RNA was annealed in 50 mM Tris HCl (pH 8.0) at 80 °C for 5 min and then chilled on ice for 10 min. For TAR-cylinder binding studies, RNA samples (2 µM) were incubated with increasing cylinder concentrations  in TK buffer , KCl (100 mM)) to a final volume of 10 µL for 30 min at RT and chilled on ice for 10 min. Samples were analysed by 15% non-denaturing polyacrylamide gel in 0.5x TB buffer (Tris HCl (40 mM), boric acid (45 mM)), pH 8.3 at 11 v/cm and 4 °C for 5 h.TAR RNA was radiolabelled at the 5′ end using T4 polynucleotide kinase and [γ-For TAR-ADP-1 inhibition assays, 10 µL of TAR RNA  were pre-incubated with ADP-1 peptide (0.3 μM) in DTT (100 mM), 0.1% Triton X-100 and Tris.HCl (50 mM pH 8.0) for 15 min followed by incubation with cylinders  for 30 min and chilled on ice for 10 min. Samples were run on a 10% non-denaturing polyacrylamide gel at 4°C for 2.5 h at 11 v/cm.All mobility shift assays were performed employing the same starting radiolabelled TAR RNA solution and all gels were imaged by developing on a white phosphor screen and scanned using a Bio-Rad personal molecular imager (PMI) employing equal exposure conditions.TAR RNA was dissolved in sodium phosphate buffer  containing EDTA (0.5 mM) and annealed by heating at 90 °C for 3 min followed by slow temperature decrease (30 min) down to 25 °C. 3 μM RNA were incubated with each cylinder (1:1 ratio) at 25 °C for 30 min (100 μL solutions). RNA melting experiments were performed by detecting absorbance at 260 nm when increasing the temperature from 25 to 95 °C, using masked microcuvettes (1 cm path length and 0.1 mL capacity) in a Cary Varian 5000 spectrophotometer equipped with a Peltier multicell block and temperature controller . Melting temperatures  were calculated within the thermal heating program by applying a first derivative calculation and final Tm values are the average from 3 independent experiments.ex 545 nm, λem 600 nm, dwell time 0.5 s) using a 3 mL quartz cuvette. The emission of TAR RNA  in the presence of Ethidium Bromide  was measured and normalized to 0% fluorescence. Increasing concentrations of cylinders were added  allowing 10 min stabilization before measuring emission. Each titration was repeated 3 times, reported as normalized variation of emission at 600 nm (ΔF) vs Cylinder/TAR ratio  and cultured in RPMI medium supplemented with 10% FBS and 1% penicillin/streptomycin. TZM-bl cells were kindly provided by Professor Bill Paxton  and cultured in DMEM medium supplemented with 10% FBS, 1% penicillin/streptomycin and 1% L-glutamine.For HIV-1 genesis and infection, 293 T cells were transfected with the pNL4.3 full-length HIV-1 plasmid using Fugene6 (Promega). Cells were transfected for 6 h at 37 °C and the media replaced with fresh DMEM supplemented with 3% FBS/1% penicillin/streptomycin. Subsequently, virus was collected at 48 h and 60 h post-transfection, the stocks were pooled, clarified by centrifugation at 2500 rpm for 10 min and stored at −80 °C. To evaluate the effect of FeCy, NiCy and RuCy on HIV-1 replication, 1G5  and TZM-bl (8′000 cells in 100 μL) were pre-treated with different concentration of cylinders  in 96 well plates, for 4 h prior to infection. Unbound virus was removed by washing and fresh media containing the cylinders was added. After 48 h cells were washed, lysed and luciferase activity measured.50 values were calculated with SigmaPlot employing at least three repeats of triplicated measurements.1G5 and TZM-bl (same cells/100 μL employed for infectivity assay) were treated with different concentration of cylinders  in 96 well plates, for 48 h at 37 °C. Cells were washed 3x with PBS and treated with MTT -2, 5-diphenyltetrazolium bromide), 50 μg in 100 μL medium  for 2 hours in incubator. The medium was removed, the purple crystals dissolved in 200 μL DMSO and absorbance at 576 nm was measured. IC6 cells in 5 mL medium) were incubated with Ru and Ni cylinder for 8 h at 37 °C (10 and 50 μM the 2 concentrations investigated). Cells were washed 3x with PBS by centrifugation, counted and cell pellets digested with nitric acid  at 90 °C overnight. Solutions were diluted with water (Trace Select grade from Sigma-Aldrich) to achieve nitric acid concentration <5% and analysed by ICP-MS spectrometry (Agilent LC-ICP-MS (7500cx) at the University of Warwick, UK).1G5 cells (5 × 1050 μM of each cylinder were incubated at 37 °C for 48 hours in the presence of 10000 cells (either TZM-Bl or 1G5) or in medium only. Incubation was carried out in 96 well plates using 100 μL volume for each sample. UV-Vis profiles were monitored at 0, 5, 24 and 48 hours by using a CLARIOstar plate reader from BMG Labtech and the final results are the average of three independent experiments.Supplementary Information


RNA-Seq is currently the most widely used tool to analyze whole-transcriptome profiles. There are numerous commercial kits available to facilitate preparing RNA-Seq libraries; however, it is still not clear how some of these kits perform in terms of: 1) ribosomal RNA removal; 2) read coverage or recovery of exonic vs. intronic sequences; 3) identification of differentially expressed genes (DEGs); and 4) detection of long non-coding RNA (lncRNA). In RNA-Seq analysis, understanding the strengths and limitations of commonly used RNA-Seq library preparation protocols is important, as this technology remains costly and time-consuming.In this study, we present a comprehensive evaluation of four RNA-Seq kits. We used three standard input protocols: Illumina TruSeq Stranded Total RNA and mRNA kits, a modified NuGEN Ovation v2 kit, and the TaKaRa SMARTer Ultra Low RNA Kit v3. Our evaluation of these kits included quality control measures such as overall reproducibility, 5′ and 3′ end-bias, and the identification of DEGs, lncRNAs, and alternatively spliced transcripts. Overall, we found that the two Illumina kits were most similar in terms of recovering DEGs, and the Illumina, modified NuGEN, and TaKaRa kits allowed identification of a similar set of DEGs. However, we also discovered that the Illumina, NuGEN and TaKaRa kits each enriched for different sets of genes.At the manufacturers’ recommended input RNA levels, all the RNA-Seq library preparation protocols evaluated were suitable for distinguishing between experimental groups, and the TruSeq Stranded mRNA kit was universally applicable to studies focusing on protein-coding gene profiles. The TruSeq protocols tended to capture genes with higher expression and GC content, whereas the modified NuGEN protocol tended to capture longer genes. The SMARTer Ultra Low RNA Kit may be a good choice at the low RNA input level, although it was inferior to the TruSeq mRNA kit at standard input level in terms of rRNA removal, exonic mapping rates and recovered DEGs. Therefore, the choice of RNA-Seq library preparation kit can profoundly affect data outcomes. Consequently, it is a pivotal parameter to consider when designing an RNA-Seq experiment.The online version of this article (10.1186/s12864-019-5953-1) contains supplementary material, which is available to authorized users. Omics technology, driven by next-generation sequencing (NGS) coupled with new and increasingly robust bioinformatics pipelines, has triggered exponential growth in the accumulation of large biological datasets. The first NGS study, published in 2005 , reporteRNA-Seq is used primarily to identify differentially expressed genes (DEGs) in different biological conditions, but it is also used to discover non-coding RNAs such as microRNAs and long non-coding RNAs (lncRNAs) . RNA-SeqThe SEQC project evaluated the sensitivity, specificity, reproducibility, and complexity of gene expression, DEGs, and splice junction detection from RNA-Seq performed at multiple sites, using the same commercial reference library and External RNA Controls Consortium (ERCC) RNA spike-in controls as well as experimental samples, but using different sequencing platforms and bioinformatics pipelines . OverallThe ABRF-NGS study evaluated not only the sensitivity, specificity, reproducibility, and complexity of gene expression, but also differential gene expression and splice junction detection among different combinations of sequencing platforms and library preparation methods, taking into account size-specific fractionation and RNA integrity . In geneSchuierer S. et al.  evaluateMore recently, increasing interest in investigating rare cell populations and detailed biological mechanisms has led to a demand for protocols generating high quality libraries from nanogram quantities of total RNA , 13 and The source of data variation among different library preparation methods remains unclear. Therefore, in the present study, we carefully compared the results we obtained from several commercial RNA-Seq library preparation kits with different rRNA depletion and cDNA synthesis methods to understand the strength of each protocol. The first goal of our study was to investigate confounding factors in RNA-Seq library preparation protocols using three standard input kits: the TruSeq Stranded Total RNA and mRNA Library Prep Kits from Illumina, and a modified NuGEN Ovation® RNA-Seq System. Defining the properties of the data generated using these protocols may aid users in designing their future RNA-Seq strategies. The second part of our study was to thoroughly evaluate the SMARTer Ultra Low RNA Kit using mouse embryonic stem cells (mESCs). Our results demonstrated that the TruSeq Stranded mRNA protocol was the best for transcriptome profiling and that the TruSeq Stranded Total RNA and mRNA protocols were comparable, whereas the modified NuGEN protocol performed less well for whole transcriptome analysis, but might be a better choice for studies focused on non-coding RNAs. Lastly, although the results obtained with the SMARTer Ultra Low RNA Kit were comparable to those of the TruSeq Stranded mRNA kit for most metrics and for identification of DEGs, the absolute expression levels were only moderately correlated. We conclude that each RNA-Seq protocol has individual strengths for particular individual applications that need to be considered for a successful RNA-Seq experiment.Zbtb24 knockout (1lox/1lox) clones compared with three wild-type (2lox/+) clones  using the TaKaRa SMARTer Ultra Low RNA protocol directly on cells with no RNA preparation step. When RNA was isolated, all total RNA samples had RNA integrity (RIN) numbers > 8.90.Figure We used the manufacturer-recommended optimal input amounts   fragments mapped to exons values were calculated to assess the concordance of the three standard RNA-Seq protocols on expression quantification. The correlation coefficients were greater than 0.97 between samples prepared using the same protocol, regardless of whether the samples were biological replicates of the same condition or from different conditions. The correlation coefficients between samples prepared using different protocols were lower: 0.93–0.97 between the TruSeq Stranded Total RNA and mRNA protocols, 0.80–0.87 between the TruSeq Stranded Total RNA and modified NuGEN protocols, and 0.77–0.82 between the TruSeq Stranded mRNA and modified NuGEN protocols  than either was with the modified NuGEN protocol  . The DEGs recovered with the TruSeq Total RNA and mRNA protocols had correlation coefficients of 0.78 and 0.76 vs. qPCR, whereas the modified NuGEN protocol had a correlation coefficient of 0.62  clones  and three wild-type (2lox/+) clones , we evaluated its performance by comparing it to that of the TruSeq Stranded mRNA protocol using 2 μg of total RNA, as a “gold standard” that represents overall robustness with regard to rRNA contamination, mRNA species representation, identification of DEGs, and overall reproducibility. The SMARTer kit protocol resulted in libraries with higher levels of rRNA contamination at both the 100 (~ 1 ng RNA) and 1000 cell (~ 10 ng RNA) levels than did the TruSeq Stranded mRNA protocol using standard input RNA amounts , indicating high reproducibility with the SMARTer Ultra Low RNA Kit protocol. However, the coefficients between samples prepared using the SMARTer and standard TruSeq Stranded mRNA protocols were lower 0.87–0.91)  and 1000 (~ 10 ng RNA) cells. Although all protocols yielded reproducible data, overall, the Illumina kits generally outperformed the modified NuGEN Ovation v2 kit at standard RNA input levels. The modified NuGEN protocol was useful for the recovery of lncRNAs and intronic sequences, but also had higher levels of rRNA contamination.One impediment to the efficient recovery of meaningful RNA-Seq data is repetitive rRNA. Nearly 80% of RNA in a cell is rRNA, making it preferable to remove this class of RNA prior to library construction . RNA-SeqThe TruSeq protocols yielded a ≥ 90% overall mapping rate for fragments with both ends mapped to the genome, compared to 60% for the modified NuGEN protocol , respectively, which was slightly fewer than the 1430 DEGs identified using the modified NuGEN protocol Fig. b. This cAlthough the SMARTer Ultra Low RNA Kit-generated libraries were able to capture the effect of biological differences between experimental and control samples, overall, its performance was inferior to that of the TruSeq Stranded mRNA protocol, given both the higher amount of rRNA recovered and the lower number of DEGs recovered Figs.  and 7. TThere are still some limitations in this study that could be addressed in future work. For example, this study didn’t include spike-in RNAs, which could serve as a sample independent benchmark to further evaluate the accuracy of DEG detection in libraries prepared by different protocols. Future work could also consider investigating additional ultralow RNA-Seq protocols and using standard RNA samples such as Universal Human Reference RNA (UHRR) for an easier comparison to other studies. In summary, all the RNA-Seq library preparation protocols evaluated in this study were suitable for distinguishing between experimental groups when using the manufacturers’ recommended amount of input RNA. However, we made some discoveries that might have been previously overlooked. First, we found that the TruSeq Stranded mRNA protocol is universally applicable to studies focusing on dissecting protein-coding gene profiles when the amount of input RNA is sufficient, whereas the modified NuGEN protocol might provide more information in studies designed to understand lncRNA profiles. Therefore, choosing the appropriate RNA-Seq library preparation protocol for recovering specific classes of RNA should be a part of the overall study design . Second,Zbtb24 knockout (1lox/1lox) clones and three Zbtb24 wild-type (2lox/+) clones were used as experimental and control samples, respectively. The mice used for this part of the study were generated in-house at MD Anderson Science Park. A manuscript describing the Zbtb24 KO mESCs, including transcriptomic profiling results, is also in preparation.The use of mice in this project has been reviewed and approved by The University of Texas MD Anderson Cancer Center (MD Anderson) IACUC committee  and . C57BL/6 mice were purchased from The Jackson Laboratory . For the three standard input RNA-Seq library preparation protocols , total RNA was isolated from three xenograft tumors  from control [30% calorie restricted diet ] and expTotal RNA from mouse xenograft tumor tissues was isolated using TRIZOL following the manufacturer’s protocol. Isolated RNA samples were treated with DNase I followed by purification with a QIAGEN RNeasy Mini kit . Total RNA from mESCs was extracted using the QIAGEN RNeasy Mini kit with on-column DNase treatment following the manufacturer’s protocol. Both concentration and quality of all the isolated RNA samples were measured and checked with an Agilent Bioanalyzer 2100 and Qubit. All RNA samples had RNA integrity numbers > 8.90. For the low-cell-input experiments, 100 cells and 1000 cells  were used directly without isolating total RNA in accordance with manufacturer recommendations.Libraries were prepared using the Illumina TruSeq Stranded Total RNA (Cat. # RS-122-2301) or mRNA (Cat. # RS-122-2101) kit according to the manufacturer’s protocol starting with 1 μg total RNA. Briefly, rRNA-depleted RNAs  or purified mRNAs (mRNA kit) were fragmented and converted to cDNA with reverse transcriptase. The resulting cDNAs were converted to double stranded cDNAs and subjected to end-repair, A-tailing, and adapter ligation. The constructed libraries were amplified using 8 cycles of PCR.Total RNA (100 ng) was converted to cDNA using the NuGEN Ovation RNA-Seq System v2 (Cat. # 7102–32) (NuGEN) following the manufacturer’s protocol . NuGEN-amplified double-stranded cDNAs were broken into ~ 180 base pair (bp) fragments by sonication with a Covaris S220 instrument . Fragmented cDNAs were processed on a SPRI-TE library construction system . Uniquely indexed NEXTflex adapters  were ligated onto each sample to allow for multiplexing. Adapter-ligated libraries were amplified  using a KAPA library amplification kit  and purified with AMPure XP beads (Beckman Coulter).mESC were lysed in the reaction buffer included in the SMARTer Ultra Low RNA Kit v3 (Cat. # 634849) . cDNA was then synthesized using the SMARTer Ultra Low RNA Kit followed by library construction using the Nextera DNA Sample Preparation Kit (Cat. # FC-131-1024) , according to the manufacturers’ protocols. We performed 10 cycles of PCR for 1000 cells (~ 10 ng RNA) (SMARTer 1000), and 18 cycles of PCR for 100 cells (~ 1 ng RNA) (SMARTer 100).Ten pM of pooled libraries were processed using a cBot (Illumina) for cluster generation before sequencing on an Illumina HiSeq 2500 (2 × 76 bp run).Reads were mapped to rRNA sequences  using Bowtie2 (version 2.1.0) . Reads tThe longest transcript from each gene was chosen to represent the gene. The reads were then mapped to all the transcript sequences using Bowtie2. Transcripts with fewer than 200 total fragment counts or shorter than 1000 bps were filtered out leaving at least 12 k transcripts for each sample. Each full-length transcript was subdivided evenly into 1000 bins. The mean coverage of fragments over each bin was normalized to the total coverage over the whole transcript and then averaged over all the transcripts. Alternatively, the coverage of fragments over each position of the 1000 bps downstream of the 5′-end or upstream of the 3′-end was normalized by the mean coverage of the whole transcript, and then averaged over all the transcripts.The number of known splicing junctions (defined as junctions with both 5′- and 3′- splice sites annotated in the reference gene set) supported by at least one read in each sample was counted using RSeQC (version 2.6.4) .Each point in a saturation curve was generated by randomly selecting the desired number of fragments and calculating the percentage of genes with more than 10 fragments over all the genes. For each sample, this procedure was repeated three times and the curve represents the average percentage of genes at each corresponding number of fragments.2(cpm + 1) values of all the genes using the dist function and Euclidean method in R, as well as the hierarchical clustering (hclust) function and complete method in R.Hierarchical clustering of samples was performed using the logThe number of fragments in each known gene from GENCODE Release M4  was enum2 ratio > 1. Then the gene expression, GC content, and gene length for the two groups of more highly expressed genes were plotted in box plots. The gene expression is the average FPKM (number of fragments per kilobase per million mapped fragments) value of all the samples used in the evaluation of the standard input or ultralow input protocols. The longest transcript representing each gene was used to calculate both gene GC content and length.Between a pair of protocols, the genes with elevated expression in one protocol compared to the other protocol were identified by edgeR at FDR < 0.01 and logAdditional file 1:Figure S1. Mapping statistics and read coverage over transcripts for all the libraries prepared from 100 ng RNA with standard input protocols prepared. A. The rRNA mapping rate was calculated as the percentage of fragments that were mappable to rRNA sequences. B. The non-rRNA mapping rate was calculated from all the non-rRNA fragments as the percentage of fragments with both ends or one end mapped to the genome. C. Multiple alignment rates were determined from non-rRNA fragments that were mapped to multiple locations of the genome. D. Read-bias was assessed using the read coverage over transcripts. Each transcript was subdivided evenly into 1000 bins and the read coverage was averaged over all the transcripts. (TIF 2856 kb)Additional file 2:Figure S2. Read coverage near the 5′- (A and C) and 3′-end (B and D) of the transcripts. The TruSeq Total RNA and mRNA libraries shown in A and B were prepared from 1 μg RNA and in C and D were prepared from 100 ng RNA. The read coverage over each position of the 1000 bps downstream of the 5′-end or upstream of the 3′-end was normalized to the mean coverage over the whole transcript, and then averaged over all the transcripts. (TIF 2833 kb)Additional file 3:Figure S3. Representation of the transcriptome for all the libraries prepared from 100 ng RNA with standard input protocols. A. Composition of the uniquely mapped fragments, shown as the percentage of fragments in exonic, intronic, and intergenic regions. According to the direction of transcription, exonic and intronic regions were further divided into sense and antisense. B. Saturation analysis showing the percentage of coding genes recovered  at increasing sequencing depth. C-D. Saturation analysis showing the percentage of lncRNAs recovered  at increasing sequencing depth. In C, the six libraries created using each of three protocols  are plotted individually. In D, the six libraries from the same protocol were pooled. E. Saturation analysis showing the number of splice junctions recovered at increasing sequencing depth. (TIF 3724 kb)Additional file 4:Figure S4. Concordance of expression quantification between the libraries prepared from 100 ng RNA with standard input protocols. A. Scatter plots in a smoothed color density representation (top-right panel) and Spearman’s rank correlation coefficients (bottom-left panel) for all pairs of libraries using log2(cpm + 1) values. B. Unsupervised clustering of all the libraries using log2(cpm + 1) values. Euclidean distance with complete linkage was used to cluster the libraries. C. Principal component analysis (PCA) of all the libraries, using log2(cpm + 1) values. The values for each gene across all the libraries were centered to zero and scaled to have unit variance before being analyzed. Circles and triangles represent control and experimental libraries, respectively . For all analyses in Fig. Additional file 5:Figre S5. Concordance of expression quantification using standard protocols with additional technical replicates prepared by the TruSeq Stranded Total RNA and mRNA protocols. A. Unsupervised clustering of all the libraries using log2(cpm + 1) values. Euclidean distance with complete linkage was used to cluster the libraries. B. Principal component analysis (PCA) for all libraries using log2(cpm + 1) values. Blue, green and red dots represent libraries prepared using the TruSeq Stranded Total RNA, TruSeq Stranded mRNA, and NuGen protocols, respectively. The darker colors represent the original libraries presented in this study, and the lighter colors are technical replicates prepared at different times. Circles and triangles represent control and experimental libraries, respectively. (TIF 2607 kb)Additional file 6:Figure S6. Supplementary to Fig. 2 ratios between tumor tissues of control and experimental mice based on DEGs. The black dots represent genes that were called as differentially expressed regardless of library preparation method, and colored dots represent genes that were called as differentially expressed with only one library preparation method. The Spearman’s rank correlation coefficient is shown at the top of the plot. (TIF 1431 kb)Additional file 7:Figure S7. Box plots of gene expression, GC content and gene length for the genes with elevated expression estimation in one protocol compared to the other protocol. Top figures are box plots of gene expression in log2(FPKM+ 1). Middle figures are box plots of GC content. Bottom figures are box plots of gene length. Panels A-C are for the standard input methods. Panels D-F are for the SMARTer Ultra Low RNA Kit. Panel A shows TruSeq mRNA protocol vs. the TruSeq Total RNA protocol. Panel B shows the TruSeq mRNA protocol vs. the modified NuGEN protocol. Panel C shows the NuGEN protocol vs. the TruSeq Total RNA protocol. Panel D shows the SMARTer Ultra Low RNA Kit 100 cells vs. 1000 cells. Panel E shows the TruSeq mRNA protocol vs. the SMARTer ultra-low protocol (100 cells). Panel F shows the TruSeq mRNA protocol vs. the SMARTer protocol (1000 cells). (TIF 2445 kb)