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.
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.
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