Small RNAs from plants, bacteria and fungi within the order Hypocreales are ubiquitous in human plasma
© Beatty et al.; licensee BioMed Central Ltd. 2014
Received: 25 April 2014
Accepted: 16 October 2014
Published: 25 October 2014
The human microbiome plays a significant role in maintaining normal physiology. Changes in its composition have been associated with bowel disease, metabolic disorders and atherosclerosis. Sequences of microbial origin have been observed within small RNA sequencing data obtained from blood samples. The aim of this study was to characterise the microbiome from which these sequences are derived.
Abundant non-human small RNA sequences were identified in plasma and plasma exosomal samples. Assembly of these short sequences into longer contigs was the pivotal novel step in ascertaining their origin by BLAST searches. Most reads mapped to rRNA sequences. The taxonomic profiles of the microbes detected were very consistent between individuals but distinct from microbiomes reported at other sites. The majority of bacterial reads were from the phylum Proteobacteria, whilst for 5 of 6 individuals over 90% of the more abundant fungal reads were from the phylum Ascomycota; of these over 90% were from the order Hypocreales. Many contigs were from plants, presumably of dietary origin. In addition, extremely abundant small RNAs derived from human Y RNAs were detected.
A characteristic profile of a subset of the human microbiome can be obtained by sequencing small RNAs present in the blood. The source and functions of these molecules remain to be determined, but the specific profiles are likely to reflect health status. The potential to provide biomarkers of diet and for the diagnosis and prognosis of human disease is immense.
KeywordsSmall RNAs Fungi Plasma Microbiome Metagenomics Next generation sequencing MicroRNA Biomarker Blood Y RNA
It has been estimated that there are at least ten times more microbial cells associated with our bodies than there are human cells [1, 2]. Recent advances in high throughput, metagenomic sequencing approaches have facilitated identification of this diverse population of microbes at the genomic level. Characterisation of this microbiome, led by the Human Microbiome Project , has revealed that its composition varies widely between body sites and between individuals [2, 4–7].
The microbiome has a significant influence upon health. The majority of microbes are found in the gut and have essential roles in normal human physiology and immune responses [1, 8]. The composition of the gut microbiome is correlated with diet  and may be linked with the pathophysiology of bowel disorders [10, 11], obesity [12–14], atherosclerosis [15–17], diabetes , rheumatoid arthritis [19, 20] and neurodevelopmental disorders . Inflammatory bowel conditions have been linked with the intestinal fungal community [4, 22].
Most metagenomic studies to date have involved isolation of DNA from external body sites or from the respiratory or digestive tracts, with fecal samples being the most commonly used source for investigation of the gut microbiome. Certain small RNAs are stable in the blood and in particular microRNAs have been widely studied as potential predictors of disease [23, 24]. However, we and others [25–27] have observed the existence of additional, exogenous small RNAs of potential microbial origin. Indeed, Wang et al. have documented the existence of RNA from bacteria and fungi in plasma and suggested that they may serve as signaling molecules or indicators of human health . The origin of these small RNAs is unclear, but they are almost certainly derived from microbes inhabiting the gut or respiratory tract, rather than from viable microbes within the circulation. Nonetheless, it seems likely that the subset of the total human microbiome which contributes to these blood-borne small RNAs is linked with health status. The ability to reliably determine the composition of this microbiome from the sequences of the small RNAs present in a blood sample could form the basis of an extremely valuable diagnostic test.
The aim of this study was to construct a profile of the microbiome from which the exogenous small RNAs present in human plasma are derived. The merging of overlapping sequences to generate contigs facilitated identification of the origin of the short RNA sequences. The microbiome profiles generated were consistent across 6 individuals (3 from this study and 3 from publicly available data ). In addition to bacterial sequences, a large proportion of reads matched fungal sequences. To our surprise, the majority of these were assigned to the order Hypocreales. This work has further demonstrated the feasibility of generating a microbiome profile from small RNAs in plasma . The ease of obtaining blood samples will facilitate analysis of this microbiome in a wide range of physiological and disease conditions. These findings also raise the intriguing questions of whether these exogenous RNAs have any functional implications and why sequences from one fungal order are so abundant.
A significant number of unannotated reads remained in all samples. The randomly cloned DNA sequences obtained in conventional metagenomic studies are typically assembled into contigs to enhance identification of homology with known genes. Although this strategy would not be applicable to discretely processed small RNAs, such as microRNAs, we reasoned that it could aid detection of longer RNAs which are processed to generate multiple small overlapping RNAs. All the unannotated reads were therefore pooled and assembled into 41542 contigs. For annotation purposes, the 5142 contigs with significant hits (E < 1×10−3) in a megablast search of the NCBI non-redundant database were assigned the identity of the top hit (lineages listed in Additional file 3). The unnannotated reads from each sample were realigned to these contigs and the proportions of reads mapping to different taxonomic categories calculated (Figure 2B-F). Most identifiable reads were assigned to Metazoa, Bacteria or Fungi. Although some metazoan reads could be derived from food , many are likely to be misassigned due to similarity with human sequences.
Highly expressed small RNAs derived from Y RNAs hY1 and hY3 have been reported in tumours and high expression in serum suggested by RT-PCR . We also observed a small number of sequences matching hY1 and hy3, but the presence of extremely abundant hY4 fragments, confirmed by RT-qPCR, was unexpected. Our ability to detect Y RNA fragments as such a large proportion of total small RNAs in this study may relate to practical details of the library preparation protocol employed, particularly the size range selected. Y RNAs form part of the RoRNP, which also contains the proteins Ro60 and La, but their function is poorly understood . They are required for chromosomal replication  and are overexpressed in tumours . It has been demonstrated that double-stranded RNA oligonucleotides comprising the stem of the Y RNA are sufficient to reconstitute DNA replication in vitro . Y RNAs are rapidly degraded during apoptosis to generate fragments similar in size to those observed in this study . Although it has been suggested that small RNAs derived from Y RNAs may act analogously to microRNAs, the formation of Y3 and Y5 RNA fragments has been shown to be Dicer independent . Given the abundance of the hY4 fragments in plasma, it is an intriguing possibility that they may have some, as yet unknown function.
The detection of microbial sequences in plasma supports previous reports of circulating enterobacterial transcripts  and the most detailed study of these sequences to date by Wang et al. , who performed extensive control experiments to rule out potential sources of contamination. However, the possibility that observations of exogenous RNAs result from contamination remains a serious concern . Spurious detection of such sequences could arise due to contamination during sample handling, library preparation or sequencing or result from errors in data analysis. It is difficult to envisage how contamination with identical sequences could occur in studies undertaken in diverse locations by independent investigators, ie as detected in this study and by Huang et al.  and Wang et al.  (Additional file 5: Figure S3). In addition, analysis of data from the sequence runs prior to those reported in this study confirmed that they were not the source of contamination. In this study reads were assembled to try to improve mapping accuracy and reduce the computational requirements for database searching. The observation of similar mapping results without assembly of the sequence reads (Additional file 6: Figure S4) supports the proposed phylogenetic origins.
The taxonomic breakdown of the originating organisms achieved with our contig-based strategy is in broad agreement with that reported by Wang et al.; Proteobacteria were the most abundant bacterial phylum in both studies, with Bacteroidetes also commonly detected, whilst Ascomycota was the most abundant phylum of Fungi in both studies. However, our data suggest an even greater predominance of Ascomycota and we can assign many of these reads down to the level of Order (Hypocreales). Whilst members of this order have occasionally been reported as opportune pathogens in immunocompromised patients , they are more commonly plant or insect parasites , while Hypocrea jecorina is a widely used source of cellulases . It is remarkable that the vast majority of fungal reads should be derived from a small number of closely related species or potentially even a single species. From where do all these sequences originate?
The composition of both the fungal and bacterial plasma microbiome detected suggests that the sequences do not result from contamination from the skin microbiome during collection of blood samples. Whilst the human skin microbiome varies widely, it is dominated by the bacterial phylum Actinobacteria (and to a lesser degree Firmicutes and Proteobacteria) [1, 5] and the fungal genus Malassezia of the Basidiomycota phylum . Reads from Actinobacteria comprised an average of 1.5% percent of bacterial reads in 5 of 6 samples and only 17.6% in the remaining sample. Firmicutes averaged 1% percent across all samples, although Proteobacteria were the most abundant (50%). With regard to fungi, only 3 contigs (91 reads) were assigned to Malassezia. It seems unlikely that contamination during sample processing could result in such similar microbiome profiles in three independent plasma small RNA datasets and across multiple library preparation methods.
Small RNA sequences have been reported to enter the circulation from the gastrointestinal tract  and pharmacological preparations of small interfering RNAs (siRNAs) have been demonstrated to cross the gut wall following oral administration [46–48]. The gut therefore seems the most likely origin for microbial plasma small RNAs. The human gut, in contrast to skin, is predominantly colonised by the bacterial phyla Bacteroidetes and Firmicutes [1, 5], and by the fungal phylum Ascomycota . It is therefore conceivable that the gut is the source, but one would not expect the observed predominance of sequences from Hypocreales. Perhaps the niche occupied by these species within the gut predisposes them to uptake into the circulation. The respiratory tract is another potential source and indeed Fusarium is one of the four most common pathogenic fungi detected, along with Candida, Aspergillus and Cryptococcus . Although some microRNAs may be absorbed from the gut unshielded to survive exposed in the circulation for several hours [31, 50], many are protected from degradation by association with lipids and proteins [51, 52] and there is some evidence that the exogenous RNAs may be similarly protected . Indeed rRNA fragments have been shown to enter argonaute protein complexes . Differential stability could contribute to over-representation of certain sequences.
In addition to RNAs of microbial origin, some sequences potentially derived from foodstuffs were detected. Notably the greatest proportion of reads matching plant sequences were found in sample 3, which was obtained from the one individual who reported following a vegetarian diet. Although it has been reported that plant microRNAs (xenomiRs) are not reliably detected in plasma after ingestion [54, 55] the possibility of genetic material from food entering the circulation is supported by the detection of plant chloroplast DNA in the blood of cows . The unequivocal assignment of significant numbers of circulating small RNAs to plant rRNA in this study raises the exciting possibility that it may be possible to quantify diet from a simple blood test.
Great care must be taken when comparing between studies because differences in sample collection and library preparation can have profound effects upon the small RNA profiles observed and the proportion of reads mapping to Y RNAs or exogenous small RNAs. Nonetheless, the detection of these same small RNAs in diverse studies confirms that they are a common feature of the circulation.
Abundant fragments derived from the non-coding hY4 RNA, but of unknown function, have been detected in human plasma. RNAs from a diverse range of microbes are also present, but the majority of fungal sequences are from species in the Order Hypocreales. This raises questions about how these exogenous RNAs reach the circulation, whether they are functional and why specific fungi are so highly represented. This work has demonstrated the feasibility of determining the microbiome that contributes small RNAs to the blood. The profile of microbial sequences detected is almost certainly influenced by the composition of the wider microbiome, particularly in the gut. Given the integral role of the human microbiome in normal health and pathology, it seems likely that knowledge of the plasma microbiome will be soon prove to be of clinical importance.
Sample collection and RNA extraction
Three healthy individuals aged 20–40 years were recruited from Belfast, N. Ireland, UK: male, Caucasian (sample 1); female, Caucasian (sample 2); and male, Indian (sample 3). All participants completed a food-frequency questionnaire which included questions on any special dietary requirements. A blood sample was taken in EDTA-treated tubes and plasma was separated immediately by centrifugation for 10 minutes at 1,000 g and subsequently at 10,000 g for 10 minutes prior to RNA extraction using a miRNeasy kit (Qiagen, Crawley, UK). RNA purity and quantity were determined using a Nanodrop spectrophotometer (Thermo Scientific) and Qubit fluorimeter (Life Technologies). RNA integrity was assessed using RNA 2000 and small RNA chips on a Bioanalyzer (Agilent).
Ethics and consent
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human participants/patients were approved by the Research Ethics Committee of the School of Medicine and Dentistry, Queen’s University Belfast (Ref:11/05v3). Written informed consent was obtained from all participants.
Small RNA libraries were prepared using a Truseq small RNA sample prep kit (Illumina) following the manufacturer’s protocol. This included size selection using a 6% PAGE Gel; the region between the custom Illumina markers was excised, corresponding to insert sizes of approximately 20–35 nucleotides. Cluster generation and sequencing with 40 nucleotide reads on a MiSeq was performed at the Trinity Genome Sequencing Laboratory, Dublin .
Sequencing data were analyzed using Genomics workbench software v5.5.1 (CLCbio, Aarhus, Denmark). After removal of adapter sequences, reads >15 bp and with at least 2 copies were aligned, allowing 2 mismatches, to miRBase (Release 19), a database of human non-coding RNA downloaded from Ensembl using Biomart  and the human genome (hg19). The remaining unannotated reads were pooled and assembled into contigs using the de novo assembly algorithm of Genomics workbench. Reads from each individual sample were then mapped back to the contigs. For subsequent phylogenetic analyses the putative origins of contig sequences were assigned using the sequence identifier (gi) numbers of the top hits determined by megablast [59, 60] (available online ) against the NCBI non-redundant database (E-value <0.001). Lists of gi numbers were uploaded to the metagenomic analysis tools  available through the Galaxy platform [63, 64], specifically to ‘Fetch taxonomic representation’, ‘Summarize taxonomy’, ‘draw phylogeny’ and ‘Find lowest diagnostic rank’. Microsoft Access databases were used to integrate datasets. Taxonomic classification of the top 5% of BLAST hits was performed using the MEtaGenome ANalyzer (MEGAN) analysis tool [32, 33]. The lowest common ancestor was assigned following manual removal of individual hits with obviously incorrect taxonomic classifications (ie matching the query and top blast hits but not other sequences from their alleged species). Optimal RNA secondary structures were predicted using the Vienna RNAfold webserver [65, 66]. Additional multiple sequence alignments were performed using the Multiple Alignment using Fast Fourier Transform (MAFFT) program  available online  or Clustal Omega [69, 70], available through the EBI server . Multiple alignments were visualised with Jalview  and phylograms with Archaeopteryx . Custom Perl scripts were used for manipulating sequence files.
Y-RNA custom small RNA Taqman assays (Life Technologies) were designed to target the following sequences: HY4_5p; GGCUGGUCCGAUGGUAGUGGGUUAUCAGAACU and HY4_3p; CCCCCCACUGCUAAAUUUGACUGGCUU . Taqman reverse transcription and PCR were performed according to the manufacturer’s instructions on a LightCycler480 platform (Roche).
For detection of Y-RNA fragments, RNA was polyadenylated using E. coli Poly(A) Polymerase I (Ambion) and reverse transcribed using Super Script III reverse transcriptase (Life Technologies) and an oligo-dt adaptor: GCGAGCACAGAATTAATACGACTCACTATAGGTTTTTTTTTTTTVN. PCR was performed using the common reverse primer GCGAGCACAGAATTAATACGAC and either an HY4_5p primer: GGCTGGTCCGATGGTAGT or HY4_3p primer: CCCCCCACTGCTAAAATTTGA. 35 cycles of PCR were performed with the following conditions 94°C 30 sec; 56°C 30 sec; 72°C 1 minute using Hotstar Taq DNA polymerase (Qiagen).
Availability of supporting data
The data sets supporting the results of this article are available in the Gene Expression Omnibus (GEO) repository . The sequencing data generated in this study has accession number GSE52981 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52981) and the publicly available plasma small RNA sequencing data  analysed has accession number GSE45722.
Thanks to Estelle Lowry for blood collection and Jayne Woodside and Margaret Dellett for helpful comments and discussion.
This work was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) (Grant number: BB/H005498/1) and the Department for Employment and Learning, Northern Ireland.
- Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, et al: A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010, 464 (7285): 59-65. 10.1038/nature08821.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang ZK, Yang YS: Upper gastrointestinal microbiota and digestive diseases. World J Gastroenterol. 2013, 19 (10): 1541-1550. 10.3748/wjg.v19.i10.1541.PubMed CentralPubMedView ArticleGoogle Scholar
- Human Microbiome Project (HMP). http://commonfund.nih.gov/Hmp/,
- Ott SJ, Kuhbacher T, Musfeldt M, Rosenstiel P, Hellmig S, Rehman A, Drews O, Weichert W, Timmis KN, Schreiber S: Fungi and inflammatory bowel diseases: Alterations of composition and diversity. Scand J Gastroenterol. 2008, 43 (7): 831-841. 10.1080/00365520801935434.PubMedView ArticleGoogle Scholar
- Cho I, Blaser MJ: The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012, 13 (4): 260-270.PubMed CentralPubMedGoogle Scholar
- Findley K, Oh J, Yang J, Conlan S, Deming C, Meyer JA, Schoenfeld D, Nomicos E, Park M, NIH Intramural Sequencing Center Comparative Sequencing Program, Kong HH, Segre JA: Topographic diversity of fungal and bacterial communities in human skin. Nature. 2013, 498 (7454): 367-370. 10.1038/nature12171.PubMed CentralPubMedView ArticleGoogle Scholar
- Faith JJ, Guruge JL, Charbonneau M, Subramanian S, Seedorf H, Goodman AL, Clemente JC, Knight R, Heath AC, Leibel RL, Rosenbaum M, Gordon JI: The long-term stability of the human gut microbiota. Science. 2013, 341 (6141): 1237439-10.1126/science.1237439.PubMed CentralPubMedView ArticleGoogle Scholar
- Honda K, Littman DR: The microbiome in infectious disease and inflammation. Annu Rev Immunol. 2012, 30: 759-795. 10.1146/annurev-immunol-020711-074937.PubMed CentralPubMedView ArticleGoogle Scholar
- Hoffmann C, Dollive S, Grunberg S, Chen J, Li H, Wu GD, Lewis JD, Bushman FD: Archaea and fungi of the human gut microbiome: correlations with diet and bacterial residents. PLoS One. 2013, 8 (6): e66019-10.1371/journal.pone.0066019.PubMed CentralPubMedView ArticleGoogle Scholar
- Simren M, Barbara G, Flint HJ, Spiegel BM, Spiller RC, Vanner S, Verdu EF, Whorwell PJ, Zoetendal EG, Rome Foundation Committee: Intestinal microbiota in functional bowel disorders: a Rome foundation report. Gut. 2013, 62 (1): 159-176. 10.1136/gutjnl-2012-302167.PubMed CentralPubMedView ArticleGoogle Scholar
- Rigsbee L, Agans R, Shankar V, Kenche H, Khamis HJ, Michail S, Paliy O: Quantitative profiling of gut microbiota of children with diarrhea-predominant irritable bowel syndrome. Am J Gastroenterol. 2012, 107 (11): 1740-1751. 10.1038/ajg.2012.287.PubMedView ArticleGoogle Scholar
- Ley RE, Turnbaugh PJ, Klein S, Gordon JI: Microbial ecology: human gut microbes associated with obesity. Nature. 2006, 444 (7122): 1022-1023. 10.1038/4441022a.PubMedView ArticleGoogle Scholar
- Greenblum S, Turnbaugh PJ, Borenstein E: Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci U S A. 2012, 109 (2): 594-599. 10.1073/pnas.1116053109.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhao L: The gut microbiota and obesity: from correlation to causality. Nat Rev Microbiol. 2013, 11: 639-647. 10.1038/nrmicro3089.PubMedView ArticleGoogle Scholar
- Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy BT, Britt EB, Fu X, Wu Y, Li L, Smith JD, DiDonato JA, Chen J, Li H, Wu GD, Lewis JD, Warrier M, Brown JM, Krauss RM, Tang WH, Bushman FD, Lusis AJ, Hazen SL: Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013, 19 (5): 576-585. 10.1038/nm.3145.PubMed CentralPubMedView ArticleGoogle Scholar
- Koren O, Spor A, Felin J, Fak F, Stombaugh J, Tremaroli V, Behre CJ, Knight R, Fagerberg B, Ley RE, Backhed F: Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc Natl Acad Sci U S A. 2011, 108 (Suppl 1): 4592-4598.PubMed CentralPubMedView ArticleGoogle Scholar
- Backhed F: Meat-metabolizing bacteria in atherosclerosis. Nat Med. 2013, 19 (5): 533-534. 10.1038/nm.3178.PubMedView ArticleGoogle Scholar
- Karlsson FH, Tremaroli V, Nookaew I, Bergstrom G, Behre CJ, Fagerberg B, Nielsen J, Backhed F: Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature. 2013, 498 (7452): 99-103. 10.1038/nature12198.PubMedView ArticleGoogle Scholar
- Wu HJ, Ivanov II, Darce J, Hattori K, Shima T, Umesaki Y, Littman DR, Benoist C, Mathis D: Gut-residing segmented filamentous bacteria drive autoimmune arthritis via T helper 17 cells. Immunity. 2010, 32 (6): 815-827. 10.1016/j.immuni.2010.06.001.PubMed CentralPubMedView ArticleGoogle Scholar
- Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C, Bielski C, Rostron T, Cerundolo V, Pamer EG, Abramson SB, Huttenhower C, Littman DR: Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. Elife. 2013, 2 (0): doi:10.7554/eLife.01202Google Scholar
- Hsiao EY, McBride SW, Hsien S, Sharon G, Hyde ER, McCue T, Codelli JA, Chow J, Reisman SE, Petrosino JF, Patterson PH, Mazmanian SK: Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell. 2013, 155 (7): 1451-1463. 10.1016/j.cell.2013.11.024.PubMed CentralPubMedView ArticleGoogle Scholar
- Iliev ID, Funari VA, Taylor KD, Nguyen Q, Reyes CN, Strom SP, Brown J, Becker CA, Fleshner PR, Dubinsky M, Rotter JI, Wang HL, McGovern DP, Brown GD, Underhill DM: Interactions between commensal fungi and the C-type lectin receptor Dectin-1 influence colitis. Science. 2012, 336 (6086): 1314-1317. 10.1126/science.1221789.PubMed CentralPubMedView ArticleGoogle Scholar
- Geekiyanage H, Jicha GA, Nelson PT, Chan C: Blood serum miRNA: non-invasive biomarkers for Alzheimer's disease. Exp Neurol. 2012, 235 (2): 491-496. 10.1016/j.expneurol.2011.11.026.PubMed CentralPubMedView ArticleGoogle Scholar
- Guay C, Roggli E, Nesca V, Jacovetti C, Regazzi R: Diabetes mellitus, a microRNA-related disease?. Transl Res. 2011, 157 (4): 253-264. 10.1016/j.trsl.2011.01.009.PubMedView ArticleGoogle Scholar
- Wang K, Li H, Yuan Y, Etheridge A, Zhou Y, Huang D, Wilmes P, Galas D: The complex exogenous RNA spectra in human plasma: an interface with human gut biota?. PLoS One. 2012, 7 (12): e51009-10.1371/journal.pone.0051009.PubMed CentralPubMedView ArticleGoogle Scholar
- Semenov DV, Baryakin DN, Kamynina TP, Kuligina EV, Richter VA: Fragments of noncoding RNA in plasma of human blood. Ann N Y Acad Sci. 2008, 1137: 130-134. 10.1196/annals.1448.030.PubMedView ArticleGoogle Scholar
- Semenov DV, Baryakin DN, Brenner EV, Kurilshikov AM, Vasiliev GV, Bryzgalov LA, Chikova ED, Filippova JA, Kuligina EV, Richter VA: Unbiased approach to profile the variety of small non-coding RNA of human blood plasma with massively parallel sequencing technology. Expert Opin Biol Ther. 2012, 12 (Suppl 1): S43-S51.PubMedView ArticleGoogle Scholar
- Huang X, Yuan T, Tschannen M, Sun Z, Jacob H, Du M, Liang M, Dittmar RL, Liu Y, Liang M, Kohli M, Thibodeau SN, Boardman L, Wang L: Characterization of human plasma-derived exosomal RNAs by deep sequencing. BMC Genomics. 2013, 14: 319-10.1186/1471-2164-14-319.PubMed CentralPubMedView ArticleGoogle Scholar
- Nicolas FE, Hall AE, Csorba T, Turnbull C, Dalmay T: Biogenesis of Y RNA-derived small RNAs is independent of the microRNA pathway. FEBS Lett. 2012, 586 (8): 1226-1230. 10.1016/j.febslet.2012.03.026.PubMedView ArticleGoogle Scholar
- Leidinger P, Backes C, Deutscher S, Schmitt K, Muller SC, Frese K, Haas J, Ruprecht K, Paul F, Stahler C, Lang CJ, Meder B, Bartfai T, Meese E, Keller A: A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biol. 2013, 14 (7): R78-10.1186/gb-2013-14-7-r78.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang L, Hou D, Chen X, Li D, Zhu L, Zhang Y, Li J, Bian Z, Liang X, Cai X, Yin Y, Wang C, Zhang T, Zhu D, Zhang D, Xu J, Chen Q, Ba Y, Liu J, Wang Q, Chen J, Wang J, Wang M, Zhang Q, Zhang J, Zen K, Zhang CY: Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA. Cell Res. 2012, 22 (1): 107-126. 10.1038/cr.2011.158.PubMed CentralPubMedView ArticleGoogle Scholar
- Huson DH, Auch AF, Qi J, Schuster SC: MEGAN analysis of metagenomic data. Genome Res. 2007, 17 (3): 377-386. 10.1101/gr.5969107.PubMed CentralPubMedView ArticleGoogle Scholar
- Huson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC: Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011, 21 (9): 1552-1560. 10.1101/gr.120618.111.PubMed CentralPubMedView ArticleGoogle Scholar
- O'Donnell K, Cigelnik E, Casper HH: Molecular phylogenetic, morphological, and mycotoxin data support reidentification of the Quorn mycoprotein fungus as Fusarium venenatum. Fungal Genet Biol. 1998, 23 (1): 57-67. 10.1006/fgbi.1997.1018.PubMedView ArticleGoogle Scholar
- Sorefan K, Pais H, Hall AE, Kozomara A, Griffiths-Jones S, Moulton V, Dalmay T: Reducing ligation bias of small RNAs in libraries for next generation sequencing. Silence. 2012, 3 (1): 4-10.1186/1758-907X-3-4. -907X-3-4PubMed CentralPubMedView ArticleGoogle Scholar
- Meiri E, Levy A, Benjamin H, Ben-David M, Cohen L, Dov A, Dromi N, Elyakim E, Yerushalmi N, Zion O, Lithwick-Yanai G, Sitbon E: Discovery of microRNAs and other small RNAs in solid tumors. Nucleic Acids Res. 2010, 38 (18): 6234-6246. 10.1093/nar/gkq376.PubMed CentralPubMedView ArticleGoogle Scholar
- Kohn M, Pazaitis N, Huttelmaier S: Why YRNAs? About versatile RNAs and their functions. Biogeosciences. 2013, 3 (1): 143-156.Google Scholar
- Christov CP, Gardiner TJ, Szuts D, Krude T: Functional requirement of noncoding Y RNAs for human chromosomal DNA replication. Mol Cell Biol. 2006, 26 (18): 6993-7004. 10.1128/MCB.01060-06.PubMed CentralPubMedView ArticleGoogle Scholar
- Christov CP, Trivier E, Krude T: Noncoding human Y RNAs are overexpressed in tumours and required for cell proliferation. Br J Cancer. 2008, 98 (5): 981-988. 10.1038/sj.bjc.6604254.PubMed CentralPubMedView ArticleGoogle Scholar
- Gardiner TJ, Christov CP, Langley AR, Krude T: A conserved motif of vertebrate Y RNAs essential for chromosomal DNA replication. RNA. 2009, 15 (7): 1375-1385. 10.1261/rna.1472009.PubMed CentralPubMedView ArticleGoogle Scholar
- Rutjes SA, van der Heijden A, Utz PJ, van Venrooij WJ, Pruijn GJ: Rapid nucleolytic degradation of the small cytoplasmic Y RNAs during apoptosis. J Biol Chem. 1999, 274 (35): 24799-24807. 10.1074/jbc.274.35.24799.PubMedView ArticleGoogle Scholar
- Zhang Y, Wiggins BE, Lawrence C, Petrick J, Ivashuta S, Heck G: Analysis of plant-derived miRNAs in animal small RNA datasets. BMC Genomics. 2012, 13: 381-10.1186/1471-2164-13-381.PubMed CentralPubMedView ArticleGoogle Scholar
- Howard DH: Pathogenic Fungi in Humans and Animals (Mycology). 2002, Boca Raton, FL, USA: CRC Press, 2View ArticleGoogle Scholar
- Berbee ML: The phylogeny of plant and animal pathogens in the Ascomycota. Physiol Mol Plant Pathol. 2001, 59 (4): 165-187. 10.1006/pmpp.2001.0355.View ArticleGoogle Scholar
- Martinez D, Berka RM, Henrissat B, Saloheimo M, Arvas M, Baker SE, Chapman J, Chertkov O, Coutinho PM, Cullen D, Danchin EG, Grigoriev IV, Harris P, Jackson M, Kubicek CP, Han CS, Ho I, Larrondo LF, de Leon AL, Magnuson JK, Merino S, Misra M, Nelson B, Putnam N, Robbertse B, Salamov AA, Schmoll M, Terry A, Thayer N, Westerholm-Parvinen A, et al: Genome sequencing and analysis of the biomass-degrading fungus Trichoderma reesei (syn. Hypocrea jecorina). Nat Biotechnol. 2008, 26 (5): 553-560. 10.1038/nbt1403.PubMedView ArticleGoogle Scholar
- Aouadi M, Tesz GJ, Nicoloro SM, Wang M, Chouinard M, Soto E, Ostroff GR, Czech MP: Orally delivered siRNA targeting macrophage Map4k4 suppresses systemic inflammation. Nature. 2009, 458 (7242): 1180-1184. 10.1038/nature07774.PubMed CentralPubMedView ArticleGoogle Scholar
- Xu J, Ganesh S, Amiji M: Non-condensing polymeric nanoparticles for targeted gene and siRNA delivery. Int J Pharm. 2012, 427 (1): 21-34. 10.1016/j.ijpharm.2011.05.036.PubMed CentralPubMedView ArticleGoogle Scholar
- Akhtar S: Oral delivery of siRNA and antisense oligonucleotides. J Drug Target. 2009, 17 (7): 491-495. 10.1080/10611860903057674.PubMedView ArticleGoogle Scholar
- Ghannoum MA, Jurevic RJ, Mukherjee PK, Cui F, Sikaroodi M, Naqvi A, Gillevet PM: Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 2010, 6 (1): e1000713-10.1371/journal.ppat.1000713.PubMed CentralPubMedView ArticleGoogle Scholar
- Witwer KW: XenomiRs and miRNA homeostasis in health and disease: evidence that diet and dietary miRNAs directly and indirectly influence circulating miRNA profiles. RNA Biol. 2012, 9 (9): 1147-1154. 10.4161/rna.21619.PubMed CentralPubMedView ArticleGoogle Scholar
- Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, Gibson DF, Mitchell PS, Bennett CF, Pogosova-Agadjanyan EL, Stirewalt DL, Tait JF, Tewari M: Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci U S A. 2011, 108 (12): 5003-5008. 10.1073/pnas.1019055108.PubMed CentralPubMedView ArticleGoogle Scholar
- Vickers KC, Palmisano BT, Shoucri BM, Shamburek RD, Remaley AT: MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol. 2011, 13 (4): 423-433. 10.1038/ncb2210.PubMed CentralPubMedView ArticleGoogle Scholar
- Wei H, Zhou B, Zhang F, Tu Y, Hu Y, Zhang B, Zhai Q: Profiling and identification of small rDNA-derived RNAs and their potential biological functions. PLoS One. 2013, 8 (2): e56842-10.1371/journal.pone.0056842.PubMed CentralPubMedView ArticleGoogle Scholar
- Witwer KW, McAlexander MA, Queen SE, Adams RJ: Real-time quantitative PCR and droplet digital PCR for plant miRNAs in mammalian blood provide little evidence for general uptake of dietary miRNAs: limited evidence for general uptake of dietary plant xenomiRs. RNA Biol. 2013, 10 (7): 1080-1086. 10.4161/rna.25246.PubMed CentralPubMedView ArticleGoogle Scholar
- Snow JW, Hale AE, Isaacs SK, Baggish AL, Chan SY: Ineffective delivery of diet-derived microRNAs to recipient animal organisms. RNA Biol. 2013, 10 (7): 1107-1116. 10.4161/rna.24909.PubMed CentralPubMedView ArticleGoogle Scholar
- Bertheau Y, Helbling JC, Fortabat MN, Makhzami S, Sotinel I, Audeon C, Nignol AC, Kobilinsky A, Petit L, Fach P, Brunschwig P, Duhem K, Martin P: Persistence of plant DNA sequences in the blood of dairy cows fed with genetically modified (Bt176) and conventional corn silage. J Agric Food Chem. 2009, 57 (2): 509-516. 10.1021/jf802262c.PubMedView ArticleGoogle Scholar
- Trinity Genome Sequencing Laboratory. http://www.medicine.tcd.ie/sequencing,
- Biomart for the Ensembl database. http://www.ensembl.org/biomart/martview/,
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410. 10.1016/S0022-2836(05)80360-2.PubMedView ArticleGoogle Scholar
- Morgulis A, Coulouris G, Raytselis Y, Madden TL, Agarwala R, Schaffer AA: Database indexing for production MegaBLAST searches. Bioinformatics. 2008, 24 (16): 1757-1764. 10.1093/bioinformatics/btn322.PubMed CentralPubMedView ArticleGoogle Scholar
- Basic Local Alignment Search Tool (BLAST) home page at NCBI. http://blast.ncbi.nlm.nih.gov/,
- Kosakovsky Pond S, Wadhawan S, Chiaromonte F, Ananda G, Chung WY, Taylor J, Nekrutenko A, Galaxy Team: Windshield splatter analysis with the Galaxy metagenomic pipeline. Genome Res. 2009, 19 (11): 2144-2153. 10.1101/gr.094508.109.PubMed CentralPubMedView ArticleGoogle Scholar
- The Galaxy project. http://usegalaxy.org,
- Goecks J, Nekrutenko A, Taylor J, Galaxy Team: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010, 11 (8): R86-10.1186/gb-2010-11-8-r86.PubMed CentralPubMedView ArticleGoogle Scholar
- Gruber AR, Lorenz R, Bernhart SH, Neubock R, Hofacker IL: The Vienna RNA websuite. Nucleic Acids Res. 2008, 36 (Web Server issue): W70-W74.PubMed CentralPubMedView ArticleGoogle Scholar
- Vienna RNAfold webserver. http://rna.tbi.univie.ac.at,
- Katoh K, Misawa K, Kuma K, Miyata T: MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002, 30 (14): 3059-3066. 10.1093/nar/gkf436.PubMed CentralPubMedView ArticleGoogle Scholar
- Multiple Alignment using Fast Fourier Transform (MAFFT). http://mafft.cbrc.jp/alignment/server/,
- Goujon M, McWilliam H, Li W, Valentin F, Squizzato S, Paern J, Lopez R: A new bioinformatics analysis tools framework at EMBL-EBI. Nucleic Acids Res. 2010, 38 (Web Server issue): W695-W699.PubMed CentralPubMedView ArticleGoogle Scholar
- Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Soding J, Thompson JD, Higgins DG: Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011, 7: 539-PubMed CentralPubMedView ArticleGoogle Scholar
- Clustal Omega on the EBI server. http://www.ebi.ac.uk/Tools/msa/clustalo/,
- Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ: Jalview Version 2–a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009, 25 (9): 1189-1191. 10.1093/bioinformatics/btp033.PubMed CentralPubMedView ArticleGoogle Scholar
- Zmasek CM, Eddy SR: ATV: display and manipulation of annotated phylogenetic trees. Bioinformatics. 2001, 17 (4): 383-384. 10.1093/bioinformatics/17.4.383.PubMedView ArticleGoogle Scholar
- Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Holko M, Ayanbule O, Yefanov A, Soboleva A: NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic Acids Res. 2010, 39 (Database issue): D1005-D1010.PubMed CentralPubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.