Identification of circular RNAs from the parental genes involved in multiple aspects of cellular metabolism in barley

RNA circularization made by head-to-tail back-splicing events is involved in the regulation of gene expression from transcriptional to post-translational levels. By exploiting RNA-Seq data and down-stream analysis, we shed light on the importance of circular RNAs in plants. The results introduce circular RNAs as novel interactors in the regulation of gene expression in plants and imply the comprehensiveness of this regulatory pathway by identifying circular RNAs for a diverse set of genes. These genes are involved in several aspects of cellular metabolism as hormonal signaling, intracellular protein sorting, carbohydrate metabolism and cell-wall biogenesis, respiration, amino acid biosynthesis, transcription and translation, and protein ubiquitination. Additionally, these parental loci of circular RNAs, from both nuclear and mitochondrial genomes, encode for different transcript classes including protein coding transcripts, microRNA, rRNA, and long non-coding/microprotein coding RNAs. The results shed light on the mitochondrial exonic circular RNAs and imply the importance of circular RNAs for regulation of mitochondrial genes. Importantly, we introduce circular RNAs in barley and elucidate their cellular-level alterations across tissues and in response to micronutrients iron and zinc. In further support of circular RNAs' functional roles in plants, we report several cases where fluctuations of circRNAs do not correlate with the levels of their parental-loci encoded linear transcripts.Keywords: circular RNAs, coding and non-coding transcripts, leaves, seeds, transfer cells, micronutrients, mitochondri

Library preparation for sequencing.

<p>Double-stranded cDNA profile after <i>in-vitro</i> linear amplification, fragmentation, and adaptor ligation is shown. Fragmentation enriched samples for ≈ 220 base-long fragments. Ligation of adaptors at both ends of the fragments changed the average length to ≈ 330 base pairs.</p

Bias structure analysis.

<p>The reference gene based-correction showed no advantage over the genome-wide correction. This can be explained by the single cell-type origin of the samples. We selected 18 reference genes with coefficient variations below 10% after analyzing 43 publicly-available barley microarray datasets including 891 samples and representing 22840 probes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.s007" target="_blank">S1 File</a>). The inter-replicate variation and inter-treatment bias were calculated using the selected genes as described previously [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.ref017" target="_blank">17</a>]. We considered all possible inter-treatment comparisons in our RNA-Seq data. There was no difference in inter-treatment bias among the single reference gene based-correction, exon read count based-correction measured as RPM, and uncorrected data. Sequential application of the corrections [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.ref017" target="_blank">17</a>] was also not efficient. However, the exon read count based-correction was used for data analysis due to the large proportion of expelled inter-replicate variations. RPM: Read count Per Million Mapped reads, Std.: Standard deviation.</p

Statistics of the comparisons.

<p>Number of significant changes (DE), repressed events (Rep), induced events (Ind), events with switched isoforms (IS), isoform switching among transcripts coding different protein isoforms (IS1), and isoform switching among transcripts only different in untranslated regions (IS2). UT: untreated samples, 6 & 24: samples of 6 h and 24 h after treatments, Fe & Zn: iron and zinc treatments.</p><p>Statistics of the comparisons.</p

Retroelements were activated by foliar applications of iron and zinc.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.s014" target="_blank">S3 Table</a> for the accession numbers and other details of events shown by numbers of 1–34. 6Fe: 6 h after iron treatment, 6Zn: 6 h after zinc treatment, 24Fe: 24 h after iron treatment, 24Zn: 24 h after zinc treatment, UT: untreated plants. For example, 24Fe/UT represents the comparison of 24Fe with UT.</p

Hormonal signaling pathways.

<p><b>(A)</b> Ethylene signaling. In contrast to iron-treated plants where the ethylene response was suppressed, zinc treatment triggered the signaling pathway. <b>(B)</b> Auxin (IAA) signaling. The treatments altered the cellular compartmentalization of auxin as well as its signaling pathways. The data reveals a very early-stage induction in auxin signaling after the treatments followed by negative feedback loops enabling auxin sequestration within organelles and cellular auxin efflux. This occurred likely due to iron surplus in the iron-treated plants as well as zinc repletion and/or continuous iron deficiency after foliar application of zinc. Accession numbers of the genes are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.s010" target="_blank">S4 File</a>. Different transcript isoforms of genes are shown by capital letters, immediately after the name of genes. Fe and Zn represent iron and zinc treatments. The samples of 6 h and 24 h after treatments are labeled by 6 and 24. UT stands for untreated plants. Comparisons of 24Fe/Untreated sample and 24Zn/Untreated sample are shown as 24Fe and 24Zn, respectively. Zinc treatment was compared with iron treatment which is shown as 6Zn/6Fe or 24Zn/24Fe. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.s008" target="_blank">S2 File</a> or the text for detailed functions of genes. AA: amino acid, IBA: indole-3-butyric acid.</p

Mapping statistics.

<p>On average, 17 million exonic-reads were mapped per replicate. Fe and Zn represent iron and zinc treatments. Samples collected at 6 h and 24 h after the treatments are labeled by 6 and 24. UT is for untreated sample. R1, R2, and R3 represent biological replicates.</p

Deciphering Mineral Homeostasis in Barley Seed Transfer Cells at Transcriptional Level

<div><p>In addition to the micronutrient inadequacy of staple crops for optimal human nutrition, a global downtrend in crop-quality has emerged from intensive breeding for yield. This trend will be aggravated by elevated levels of the greenhouse gas carbon dioxide. Therefore, crop biofortification is inevitable to ensure a sustainable supply of minerals to the large part of human population who is dietary dependent on staple crops. This requires a thorough understanding of plant-mineral interactions due to the complexity of mineral homeostasis. Employing RNA sequencing, we here communicate transfer cell specific effects of excess iron and zinc during grain filling in our model crop plant barley. Responding to alterations in mineral contents, we found a long range of different genes and transcripts. Among them, it is worth to highlight the auxin and ethylene signaling factors <i>Arf</i>s, <i>Abcb</i>s, <i>Cand</i>1, <i>Hps</i>4, <i>Hac</i>1, <i>Ecr</i>1, and <i>Ctr</i>1, diurnal fluctuation components <i>Sdg</i>2, <i>Imb</i>1, <i>Lip</i>1, and <i>Phy</i>C, retroelements, sulfur homeostasis components <i>Amp</i>1, <i>Hmt</i>3, <i>Eil</i>3, and <i>Vip</i>1, mineral trafficking components <i>Med</i>16, <i>Cnnm</i>4, <i>Aha</i>2, <i>Clpc</i>1, and <i>Pcbp</i>s, and vacuole organization factors <i>Ymr</i>155W, <i>Rab</i>G3F, <i>Vps</i>4, and <i>Cbl</i>3. Our analysis introduces new interactors and signifies a broad spectrum of regulatory levels from chromatin remodeling to intracellular protein sorting mechanisms active in the plant mineral homeostasis. The results highlight the importance of storage proteins in metal ion toxicity-resistance and chelation. Interestingly, the protein sorting and recycling factors <i>Exo</i>c7, <i>Cdc</i>1, <i>Sec</i>23A, and <i>Rab</i>11A contributed to the response as well as the polar distributors of metal-transporters ensuring the directional flow of minerals. Alternative isoform switching was found important for plant adaptation and occurred among transcripts coding for identical proteins as well as transcripts coding for protein isoforms. We also identified differences in the alternative-isoform preference between the treatments, indicating metal-affinity shifts among isoforms of metal transporters. Most important, we found the zinc treatment to impair both photosynthesis and respiration. A wide range of transcriptional changes including stress-related genes and negative feedback loops emphasize the importance to withhold mineral contents below certain cellular levels which otherwise might lead to agronomical impeding side-effects. By illustrating new mechanisms, genes, and transcripts, this report provides a solid platform towards understanding the complex network of plant mineral homeostasis.</p></div

Expression changes in metal transporters.

<p>The treatments compelled the cells to restrain the iron or zinc uptake routes across the plasma membrane and chloroplast envelope and to sequester the minerals within vacuoles. In parallel, uptake and internal-source utilization of other metals were enhanced. This indicates a strong dependency of cellular levels of minerals on each other and sheds light on the negative feedback loops to avoid excess mineral contents. Accession numbers of the genes are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.s010" target="_blank">S4 File</a>. Different transcript isoforms of genes are shown by capital letters, immediately after the name of genes. Fe and Zn represent iron and zinc treatments. The samples of 6 h and 24 h after treatments are represented by 6 and 24. UT stands for untreated plants. Comparisons of 24Fe/Untreated sample and 24Zn/Untreated sample are shown as 24Fe and 24Zn, respectively. Zinc treatment was compared with iron treatment which is shown as 6Zn/6Fe or 24Zn/24Fe. DMA: deoxymugineic acid, GS: glutathione, NA: nicotianamine.</p

SAM plays an important role in mineral homeostasis.

<p>SAM biosynthesis was enhanced by iron. HMT catalyzes SAM biosynthesis by methyl transfer to homocysteine [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.ref053" target="_blank">53</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.ref054" target="_blank">54</a>]. The suppression of PA biosynthesis could results in a large pool of substrate “SAM” in favor of NA biosynthesis after iron treatment. Not only PA biosynthesis but also NA biosynthesis was suppressed by zinc. Concurrently, glutathione synthetase (<i>Gsh-s</i>) was induced. Accession numbers of the genes are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141398#pone.0141398.s010" target="_blank">S4 File</a>. Fe and Zn represent iron and zinc treatments. The samples of 6 h and 24 h after treatments are represented by 6 and 24. UT stands for untreated plants. Comparisons of 24Fe/Untreated sample and 24Zn/Untreated sample are shown as 24Fe and 24Zn, respectively. Zinc treatment was compared with iron treatment which is shown as 6Zn/6Fe or 24Zn/24Fe. HMT: homocysteine S-methyltransferase, F-MGlu: folate monoglutamate, F-PolyGlu: folate polyglutamate, NA: nicotianamine, PA: polyamine, SAH: S-adenosylhomocysteine, SAM: S-adenosylmethionine.</p