CoLIde: a bioinformatics tool for CO-expression-based small RNA Loci Identification using high-throughput sequencing data

Application of annotation-agnostic RNA sequencing data analysis tools for biomarker discovery in liquid biopsy

RNA sequencing analysis is an important field in the study of extracellular vesicles (EVs), as these particles contain a variety of RNA species that may have diagnostic, prognostic and predictive value. Many of the bioinformatics tools currently used to analyze EV cargo rely on third-party annotations. Recently, analysis of unannotated expressed RNAs has become of interest, since these may provide complementary information to traditional annotated biomarkers or may help refine biological signatures used in machine learning by including unknown regions. Here we perform a comparative analysis of annotation-free and classical read-summarization tools for the analysis of RNA sequencing data generated for EVs isolated from persons with amyotrophic lateral sclerosis (ALS) and healthy donors. Differential expression analysis and digital-droplet PCR validation of unannotated RNAs also confirmed their existence and demonstrates the usefulness of including such potential biomarkers in transcriptome analysis. We show that find-then-annotate methods perform similarly to standard tools for the analysis of known features, and can also identify unannotated expressed RNAs, two of which were validated as overexpressed in ALS samples. We demonstrate that these tools can therefore be used for a stand-alone analysis or easily integrated into current workflows and may be useful for re-analysis as annotations can be integrated post hoc.

noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimizing technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.

Gene expression during larval caste determination and differentiation in intermediately eusocial bumblebees, and a comparative analysis with advanced eusocial honeybees

The queen‐worker caste system of eusocial insects represents a prime example of developmental polyphenism (environmentally‐induced phenotypic polymorphism) and is intrinsic to the evolution of advanced eusociality. However, the comparative molecular basis of larval caste determination and subsequent differentiation in the eusocial Hymenoptera remains poorly known. To address this issue within bees, we profiled caste‐associated gene expression in female larvae of the intermediately eusocial bumblebee Bombus terrestris. In B. terrestris, female larvae experience a queen‐dependent period during which their caste fate as adults is determined followed by a nutrition‐sensitive period also potentially affecting caste fate but for which the evidence is weaker. We used mRNA‐seq and qRT‐PCR validation to isolate genes differentially expressed between each caste pathway in larvae at developmental stages before and after each of these periods. We show that differences in gene expression between caste pathways are small in totipotent larvae, then peak after the queen‐dependent period. Relatively few novel (i.e., taxonomically‐restricted) genes were differentially expressed between castes, though novel genes were significantly enriched in late‐instar larvae in the worker pathway. We compared sets of caste‐associated genes in B. terrestris with those reported from the advanced eusocial honeybee, Apis mellifera, and found significant but relatively low levels of overlap of gene lists between the two species. These results suggest both the existence of low numbers of shared toolkit genes and substantial divergence in caste‐associated genes between Bombus and the advanced eusocial Apis since their last common eusocial ancestor.

Disentangling sRNA-Seq data to study RNA communication between species

Many organisms exchange small RNAs (sRNAs) during their interactions, that can target or bolster defense strategies in host-pathogen systems. Current sRNA-Seq technology can determine the sRNAs present in any symbiotic system, but there are very few bioinformatic tools available to interpret the results. We show that one of the biggest challenges comes from sequences that map equally well to the genomes of both interacting organisms. This arises due to the small size of the sRNAs compared to large genomes, and because a large portion of sequenced sRNAs come from genomic regions that encode highly conserved miRNAs, rRNAs or tRNAs. Here, we present strategies to disentangle sRNA-Seq data from samples of communicating organisms, developed using diverse plant and animal species that are known to receive or exchange RNA with their symbionts. We show that sequence assembly, both de novo and genome-guided, can be used for these sRNA-Seq data, greatly reducing the ambiguity of mapping reads. Even confidently mapped sequences can be misleading, so we further demonstrate the use of differential expression strategies to determine true parasite-derived sRNAs within host cells. We validate our methods on new experiments designed to probe the nature of the extracellular vesicle sRNAs from the parasitic nematode Heligmosomoides bakeri that get into mouse intestinal epithelial cells.

PAREameters: a tool for computational inference of plant miRNA-mRNA targeting rules using small RNA and degradome sequencing data

MicroRNAs (miRNAs) are short, non-coding RNAs that modulate the translation-rate of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to sequence-specific targets. In plants, this typically results in cleavage and subsequent degradation of the mRNA. Degradome sequencing is a high-throughput technique developed to capture cleaved mRNA fragments and thus can be used to support miRNA target prediction. The current criteria used for miRNA target prediction were inferred on a limited number of experimentally validated A. thaliana interactions and were adapted to fit these specific interactions; thus, these fixed criteria may not be optimal across all datasets (organisms, tissues or treatments). We present a new tool, PAREameters, for inferring targeting criteria from small RNA and degradome sequencing datasets. We evaluate its performance using a more extensive set of experimentally validated interactions in multiple A. thaliana datasets. We also perform comprehensive analyses to highlight and quantify the differences between subsets of miRNA-mRNA interactions in model and non-model organisms. Our results show increased sensitivity in A. thaliana when using the PAREameters inferred criteria and that using data-driven criteria enables the identification of additional interactions that further our understanding of the RNA silencing pathway in both model and non-model organisms.

The UEA sRNA Workbench (version 4.4): a comprehensive suite of tools for analyzing miRNAs and sRNAs

Motivation

RNA interference, a highly conserved regulatory mechanism, is mediated via small RNAs (sRNA). Recent technical advances enabled the analysis of larger, complex datasets and the investigation of microRNAs and the less known small interfering RNAs. However, the size and intricacy of current data requires a comprehensive set of tools, able to discriminate the patterns from the low-level, noise-like, variation; numerous and varied suggestions from the community represent an invaluable source of ideas for future tools, the ability of the community to contribute to this software is essential.

Results

We present a new version of the UEA sRNA Workbench, reconfigured to allow an easy insertion of new tools/workflows. In its released form, it comprises of a suite of tools in a user-friendly environment, with enhanced capabilities for a comprehensive processing of sRNA-seq data e.g. tools for an accurate prediction of sRNA loci (CoLIde) and miRNA loci (miRCat2), as well as workflows to guide the users through common steps such as quality checking of the input data, normalization of abundances or detection of differential expression represent the first step in sRNA-seq analyses.

Availability and implementation

The UEA sRNA Workbench is available at: http://srna-workbench.cmp.uea.ac.uk. The source code is available at: https://github.com/sRNAworkbenchuea/UEA_sRNA_Workbench

Supplementary information

Supplementary data are available at Bioinformatics online.

Small RNAs from the plant pathogenic fungus Sclerotinia sclerotiorum highlight host candidate genes associated with quantitative disease resistance

Fungal plant pathogens secrete effector proteins and metabolites to cause disease. Additionally, some species transfer small RNAs (sRNAs) into plant cells to silence host mRNAs through complementary base pairing and suppress plant immunity. The fungus Sclerotinia sclerotiorum infects over 600 plant species, but little is known about the molecular processes that govern interactions with its many hosts. In particular, evidence for the production of sRNAs by S. sclerotiorum during infection is lacking. We sequenced sRNAs produced by S. sclerotiorum in vitro and during infection of two host species, Arabidopsis thaliana and Phaseolus vulgaris. We found that S. sclerotiorum produces at least 374 distinct highly abundant sRNAs during infection, mostly originating from repeat-rich plastic genomic regions. We predicted the targets of these sRNAs in A. thaliana and found that these genes were significantly more down-regulated during infection than the rest of the genome. Predicted targets of S. sclerotiorum sRNAs in A. thaliana were enriched for functional domains associated with plant immunity and were more strongly associated with quantitative disease resistance in a genome-wide association study (GWAS) than the rest of the genome. Mutants in A. thaliana predicted sRNA target genes SERK2 and SNAK2 were more susceptible to S. sclerotiorum than wild-type, suggesting that S. sclerotiorum sRNAs may contribute to the silencing of immune components in plants. The prediction of fungal sRNA targets in plant genomes can be combined with other global approaches, such as GWAS, to assist in the identification of plant genes involved in quantitative disease resistance.

Small RNA populations revealed by blocking rRNA fragments in Drosophila melanogaster reproductive tissues

RNA interference (RNAi) is a complex and highly conserved regulatory mechanism mediated via small RNAs (sRNAs). Recent technical advances in high throughput sequencing have enabled an increasingly detailed analysis of sRNA abundances and profiles in specific body parts and tissues. This enables investigations of the localized roles of microRNAs (miRNAs) and small interfering RNAs (siRNAs). However, variation in the proportions of non-coding RNAs in the samples being compared can hinder these analyses. Specific tissues may vary significantly in the proportions of fragments of longer non-coding RNAs (such as ribosomal RNA or transfer RNA) present, potentially reflecting tissue-specific differences in biological functions. For example, in Drosophila, some tissues contain a highly abundant 30nt rRNA fragment (the 2S rRNA) as well as abundant 5’ and 3’ terminal rRNA fragments. These can pose difficulties for the construction of sRNA libraries as they can swamp the sequencing space and obscure sRNA abundances. Here we addressed this problem and present a modified “rRNA blocking” protocol for the construction of high-definition (HD) adapter sRNA libraries, in D. melanogaster reproductive tissues. The results showed that 2S rRNAs targeted by blocking oligos were reduced from >80% to < 0.01% total reads. In addition, the use of multiple rRNA blocking oligos to bind the most abundant rRNA fragments allowed us to reveal the underlying sRNA populations at increased resolution. Side-by-side comparisons of sequencing libraries of blocked and non-blocked samples revealed that rRNA blocking did not change the miRNA populations present, but instead enhanced their abundances. We suggest that this rRNA blocking procedure offers the potential to improve the in-depth analysis of differentially expressed sRNAs within and across different tissues.

Evolution of flower color pattern through selection on regulatory small RNAs

Small RNAs (sRNAs) regulate genes in plants and animals. Here, we show that population-wide differences in color patterns in snapdragon flowers are caused by an inverted duplication that generates sRNAs. The complexity and size of the transcripts indicate that the duplication represents an intermediate on the pathway to microRNA evolution. The sRNAs repress a pigment biosynthesis gene, creating a yellow highlight at the site of pollinator entry. The inverted duplication exhibits steep clines in allele frequency in a natural hybrid zone, showing that the allele is under selection. Thus, regulatory interactions of evolutionarily recent sRNAs can be acted upon by selection and contribute to the evolution of phenotypic diversity.

MicroRNA expression in a phosphaturic mesenchymal tumour

Phosphaturic mesenchymal tumours are a heterogeneous set of bone and soft tissue neoplasms that can cause a number of paraneoplastic syndromes such as tumour induced osteomalacia. The term phosphaturic comes from the common finding that these tumours secrete high levels of fibroblast growth factor 23 which causes renal phosphate wasting leading to hypophosphatemia. Phosphaturic mesenchymal tumours are rare and diagnosis is difficult. A very active 68 year old male presented with bone pain and muscle weakness. He was hypophosphataemic and total alkaline phosphatase was markedly elevated. The patient was placed on vitamin D supplementation but his condition progressed. In the fifth year of presentation the patient required the use of a wheelchair and described “explosive” bone pain on physical contact. Serum 1,25 dihydroxyvitamin D was low and serum fibroblast growth factor 23 was significantly elevated, raising suspicion of a phosphaturic mesenchymal tumour. A lesion was detected in his left femoral head and the patient underwent a total hip replacement. The patient displayed a rapid improvement to his condition and during a three year follow up period he returned to an active lifestyle. As molecular testing may help provide a robust diagnosis and is particularly useful in rare diseases we took a next generation sequencing approach to identify a differential expression of small RNAs in the resected tumour. Small RNAs are non-coding RNA molecules that play a key role in regulation of gene expression and can be used as specific biomarkers. We found an upregulation of miR-197. We also found a downregulation of miR-20b, miR-144 and miR-335 which is a small RNA profile typical of osteosarcoma. MiR-21, the most frequently upregulated microRNA in cancer, was downregulated. We conclude that the specific small RNA profile is typical of osteosarcoma except for the downregulation of oncogenic miR-21. Transcriptional plasticity of miR-197, which is computationally predicted to target fibroblast growth factor 23 messenger RNA, may be upregulated in a cellular effort to correct the ectopic expression of the protein.

miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets

MOTIVATION

MicroRNAs are a class of ∼21-22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure.

RESULTS

We test miRCat2 on a number of animal and plant datasets and present a comparative analysis with miRCat, miRDeep2, miRPlant and miReap. We also use mutants in the miRNA biogenesis pathway to evaluate the predictions of these tools. Results indicate that miRCat2 has an improved accuracy compared with other methods tested. Moreover, miRCat2 predicts several new miRNAs that are differentially expressed in wild-type versus mutants in the miRNA biogenesis pathway.

AVAILABILITY AND IMPLEMENTATION

miRCat2 is part of the UEA small RNA Workbench and is freely available from http://srna-workbench.cmp.uea.ac.uk/.

CONTACT

v.moulton@uea.ac.uk or s.moxon@uea.ac.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparison of alternative approaches for analysing multi-level RNA-seq data

RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments.

Comprehensive processing of high-throughput small RNA sequencing data including quality checking, normalization, and differential expression analysis using the UEA sRNA Workbench

Recently, high-throughput sequencing (HTS) has revealed compelling details about the small RNA (sRNA) population in eukaryotes. These 20 to 25 nt noncoding RNAs can influence gene expression by acting as guides for the sequence-specific regulatory mechanism known as RNA silencing. The increase in sequencing depth and number of samples per project enables a better understanding of the role sRNAs play by facilitating the study of expression patterns. However, the intricacy of the biological hypotheses coupled with a lack of appropriate tools often leads to inadequate mining of the available data and thus, an incomplete description of the biological mechanisms involved. To enable a comprehensive study of differential expression in sRNA data sets, we present a new interactive pipeline that guides researchers through the various stages of data preprocessing and analysis. This includes various tools, some of which we specifically developed for sRNA analysis, for quality checking and normalization of sRNA samples as well as tools for the detection of differentially expressed sRNAs and identification of the resulting expression patterns. The pipeline is available within the UEA sRNA Workbench, a user-friendly software package for the processing of sRNA data sets. We demonstrate the use of the pipeline on a H. sapiens data set; additional examples on a B. terrestris data set and on an A. thaliana data set are described in the Supplemental Information A comparison with existing approaches is also included, which exemplifies some of the issues that need to be addressed for sRNA analysis and how the new pipeline may be used to do this.

Profile and functional analysis of small RNAs derived from Aspergillus fumigatus infected with double-stranded RNA mycoviruses

Background

Mycoviruses are viruses that naturally infect and replicate in fungi. Aspergillus fumigatus, an opportunistic pathogen causing fungal lung diseases in humans and animals, was recently shown to harbour several different types of mycoviruses. A well-characterised defence against virus infection is RNA silencing. The A. fumigatus genome encodes essential components of the RNA silencing machinery, including Dicer, Argonaute and RNA-dependent RNA polymerase (RdRP) homologues. Active silencing of double-stranded (ds)RNA and the generation of small RNAs (sRNAs) has been shown for several mycoviruses and it is anticipated that a similar mechanism will be activated in A. fumigatus isolates infected with mycoviruses.

Results

To investigate the existence and nature of A. fumigatus sRNAs, sRNA-seq libraries of virus-free and virus-infected isolates were created using Scriptminer adapters and compared. Three dsRNA viruses were investigated: Aspergillus fumigatus partitivirus-1 (AfuPV-1, PV), Aspergillus fumigatus chrysovirus (AfuCV, CV) and Aspergillus fumigatus tetramycovirus-1 (AfuTmV-1, NK) which were selected because they induce phenotypic changes such as coloration and sectoring. The dsRNAs of all three viruses, which included two conventionally encapsidated ones PV and CV and one unencapsidated example NK, were silenced and yielded characteristic vsiRNAs together with co-incidental silencing of host fungal genes which shared sequence homology with the viral genomes.

Conclusions

Virus-derived sRNAs were detected and characterised in the presence of virus infection. Differentially expressed A. fumigatus microRNA-like (miRNA-like) sRNAs and small interfering RNAs (siRNAs) were detected and validated. Host sRNA loci which were differentially expressed as a result of virus infection were also identified. To our knowledge, this is the first study reporting the sRNA profiles of A. fumigatus isolates.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3773-8) contains supplementary material, which is available to authorized users.

MicroRNAs Associated with Caste Determination and Differentiation in a Primitively Eusocial Insect

In eusocial Hymenoptera (ants, bees and wasps), queen and worker adult castes typically arise via environmental influences. A fundamental challenge is to understand how a single genome can thereby produce alternative phenotypes. A powerful approach is to compare the molecular basis of caste determination and differentiation along the evolutionary trajectory between primitively and advanced eusocial species, which have, respectively, relatively undifferentiated and strongly differentiated adult castes. In the advanced eusocial honeybee, Apis mellifera, studies suggest that microRNAs (miRNAs) play an important role in the molecular basis of caste determination and differentiation. To investigate how miRNAs affect caste in eusocial evolution, we used deep sequencing and Northern blots to isolate caste-associated miRNAs in the primitively eusocial bumblebee Bombus terrestris. We found that the miRNAs Bte-miR-6001-5p and -3p are more highly expressed in queen- than in worker-destined late-instar larvae. These are the first caste-associated miRNAs from outside advanced eusocial Hymenoptera, so providing evidence for caste-associated miRNAs occurring relatively early in eusocial evolution. Moreover, we found little evidence that miRNAs previously shown to be associated with caste in A. mellifera were differentially expressed across caste pathways in B. terrestris, suggesting that, in eusocial evolution, the caste-associated role of individual miRNAs is not conserved.

The UEA Small RNA Workbench: A Suite of Computational Tools for Small RNA Analysis

RNA silencing (RNA interference, RNAi) is a complex, highly conserved mechanism mediated by short, typically 20-24 nt in length, noncoding RNAs known as small RNAs (sRNAs). They act as guides for the sequence-specific transcriptional and posttranscriptional regulation of target mRNAs and play a key role in the fine-tuning of biological processes such as growth, response to stresses, or defense mechanism.High-throughput sequencing (HTS) technologies are employed to capture the expression levels of sRNA populations. The processing of the resulting big data sets facilitated the computational analysis of the sRNA patterns of variation within biological samples such as time point experiments, tissue series or various treatments. Rapid technological advances enable larger experiments, often with biological replicates leading to a vast amount of raw data. As a result, in this fast-evolving field, the existing methods for sequence characterization and prediction of interaction (regulatory) networks periodically require adapting or in extreme cases, a complete redesign to cope with the data deluge. In addition, the presence of numerous tools focused only on particular steps of HTS analysis hinders the systematic parsing of the results and their interpretation.The UEA small RNA Workbench (v1-4), described in this chapter, provides a user-friendly, modular, interactive analysis in the form of a suite of computational tools designed to process and mine sRNA datasets for interesting characteristics that can be linked back to the observed phenotypes. First, we show how to preprocess the raw sequencing output and prepare it for downstream analysis. Then we review some quality checks that can be used as a first indication of sources of variability between samples. Next we show how the Workbench can provide a comparison of the effects of different normalization approaches on the distributions of expression, enhanced methods for the identification of differentially expressed transcripts and a summary of their corresponding patterns. Finally we describe individual analysis tools such as PAREsnip, for the analysis of PARE (degradome) data or CoLIde for the identification of sRNA loci based on their expression patterns and the visualization of the results using the software. We illustrate the features of the UEA sRNA Workbench on Arabidopsis thaliana and Homo sapiens datasets.

Noncoding RNA: Current Deep Sequencing Data Analysis Approaches and Challenges

One of the most significant biological discoveries of the last decade is represented by the reality that the vast majority of the transcribed genomic output comprises diverse classes of noncoding RNAs (ncRNAs) that may play key roles and/or be affected by many biochemical cellular processes (i.e., RNA editing), with implications in human health and disease. With 90% of the human genome being transcribed and novel classes of ncRNA emerging (tRNA-derived small RNAs and circular RNAs among others), the great majority of the human transcriptome suggests that many important ncRNA functions/processes are yet to be discovered. An approach to filling such vast void of knowledge has been recently provided by the increasing application of next-generation sequencing (NGS), offering the unprecedented opportunity to obtain a more accurate profiling with higher resolution, increased throughput, sequencing depth, and low experimental complexity, concurrently posing an increasing challenge in terms of efficiency, accuracy, and usability of data analysis software. This review provides an overview of ncRNAs, NGS technology, and the most recent/popular computational approaches and the challenges they attempt to solve, which are essential to a more sensitive and comprehensive ncRNA annotation capable of furthering our understanding of this still vastly uncharted genomic territory.

Role of bioinformatics in establishing microRNAs as modulators of abiotic stress responses: the new revolution

microRNAs (miRs) are a class of 21-24 nucleotide long non-coding RNAs responsible for regulating the expression of associated genes mainly by cleavage or translational inhibition of the target transcripts. With this characteristic of silencing, miRs act as an important component in regulation of plant responses in various stress conditions. In recent years, with drastic change in environmental and soil conditions different type of stresses have emerged as a major challenge for plants growth and productivity. The identification and profiling of miRs has itself been a challenge for research workers given their small size and large number of many probable sequences in the genome. Application of computational approaches has expedited the process of identification of miRs and their expression profiling in different conditions. The development of High-Throughput Sequencing (HTS) techniques has facilitated to gain access to the global profiles of the miRs for understanding their mode of action in plants. Introduction of various bioinformatics databases and tools have revolutionized the study of miRs and other small RNAs. This review focuses the role of bioinformatics approaches in the identification and study of the regulatory roles of plant miRs in the adaptive response to stresses.

Small RNA analysis in Sindbis virus infected human HEK293 cells

INTRODUCTION

In contrast to the defence mechanism of RNA interference (RNAi) in plants and invertebrates, its role in the innate response to virus infection of mammals is a matter of debate. Since RNAi has a well-established role in controlling infection of the alphavirus Sindbis virus (SINV) in insects, we have used this virus to investigate the role of RNAi in SINV infection of human cells.

RESULTS

SINV AR339 and TR339-GFP were adapted to grow in HEK293 cells. Deep sequencing of small RNAs (sRNAs) early in SINV infection (4 and 6 hpi) showed low abundance (0.8%) of viral sRNAs (vsRNAs), with no size, sequence or location specific patterns characteristic of Dicer products nor did they possess any discernible pattern to ascribe to a specific RNAi biogenesis pathway. This was supported by multiple variants for each sequence, and lack of hot spots along the viral genome sequence. The abundance of the best defined vsRNAs was below the limit of Northern blot detection. The adaptation of the virus to HEK293 cells showed little sequence changes compared to the reference; however, a SNP in E1 gene with a preference from G to C was found. Deep sequencing results showed little variation of expression of cellular microRNAs (miRNAs) at 4 and 6 hpi compared to uninfected cells. Twelve miRNAs exhibiting some minor differential expression by sequencing, showed no difference in expression by Northern blot analysis.

CONCLUSIONS

We show that, unlike SINV infection of invertebrates, generation of Dicer-dependent svRNAs and change in expression of cellular miRNAs were not detected as part of the Human response to SINV.