The effect of Epichloë on the microbiota of established perennial ryegrass pastures

Perennial ryegrass (Lolium perenne) is a critical agricultural plant supporting New Zealand's intensive pasture-based dairy industry. A significant breakthrough in perennial ryegrass research was the discovery of Epichloë, a genus of ascomycete fungi that form an endophytic symbiosis with grasses and reduce invertebrate pest damage. Plant breeders have incorporated several strains of Epichloë in perennial ryegrass, including AR1 and AR37, which exhibit variations in their alkaloid production to target different pasture pests. The impact of Epichloë on ryegrass production and invertebrate pests has been extensively studied; however, little attention has focussed on its effects on other associated microorganisms, particularly in established pastures. This study utilised 16S and ITS rRNA amplicon sequencing to determine the influence of two Epichloë strains, AR1, AR37, and a Nil (without Epichloë) treatment, on the bacterial and fungal communities in three-year-old perennial ryegrass swards from three locations in New Zealand. The results revealed that the ryegrass niche and farming site were the most significant factors driving microbiome variation. Epichloë strain did not consistently correlate to variation across bacterial and fungal communities across all sites, except for the shoot endosphere communities from the one Canterbury farming location sampled (Burnham). Epichloë treatment could, however, be associated with several differences in the abundance of individual fungal ASVs across the sites. This study unveils the diverse microbiota within established ryegrass pastures. The presence of core microbiota across all samples despite differences in Epichloë strain was reassuring to plant breeders in that the wide utilisation of Epichloë in New Zealand pastures could not be associated with any dramatic community differences across the microbiome of three-year-old established pastures. Any Epichloë effects on plant microbiome were subtle and site-dependent in plants surviving 3 years post-sowing. This information can assist future research on microbial solutions and plant breeding targets for mitigating soil diseases and poor ryegrass persistence across different farming regions in New Zealand.</p

Data_Sheet_1_Integrative analysis of RNA-sequencing and microarray for the identification of adverse effects of UVB exposure on human skin.docx

BackgroundUltraviolet B (UVB) from sunlight represents a major environmental factor that causes toxic effects resulting in structural and functional cutaneous abnormalities in most living organisms. Although numerous studies have indicated the biological mechanisms linking UVB exposure and cutaneous manifestations, they have typically originated from a single study performed under limited conditions.MethodsWe accessed all publicly accessible expression data of various skin cell types exposed to UVB, including skin biopsies, keratinocytes, and fibroblasts. We performed biological network analysis to identify the molecular mechanisms and identify genetic biomarkers.ResultsWe interpreted the inflammatory response and carcinogenesis as major UVB-induced signaling alternations and identified three candidate biomarkers (IL1B, CCL2, and LIF). Moreover, we confirmed that these three biomarkers contribute to the survival probability of patients with cutaneous melanoma, the most aggressive and lethal form of skin cancer.ConclusionOur findings will aid the understanding of UVB-induced cutaneous toxicity and the accompanying molecular mechanisms. In addition, the three candidate biomarkers that change molecular signals due to UVB exposure of skin might be related to the survival rate of patients with cutaneous melanoma.</p

Chemical inhibition of VEGF signaling by cediranib reduces ERMS growth <i>in vivo</i>.

<p>Syngeneic CG1 fish were transplanted with ERMS cells that co-expressed <i>rag2-KRASG12D</i> and <i>rag2-dsRED</i>. Fish with engrafted tumors were treated with DMSO vehicle (A–F) or 100 nM of cediranib for 7 days (G–L). Pre-treatment (A–C and G–I) and post-treatment images (D–F and J–L) of representative fish. Bright field (A,D,G,J), dsRED fluorescence (B,E,H,K) and merged image planes (C,F,I,L). Scale bar is 3 mm. (M) Quantification of relative volume change for individual animals. (N–O) <i>fli1-GFP</i> transgenic zebrafish were transplanted with dsRED-labeled ERMS and treated with DMSO (N) and cediranib (O). Scale bar equals 50 µm. (P) Microvessel density quantification. Asterisk indicates statistically significant difference between DMSO and cediranib-treated groups based on student t-test. Each error bar indicates standard deviation from 3 fields of microvessels for each animal. EDU incorporation analysis in DMSO (Q) or cediranib (R) treated fish. Scale bar is 50 µm. (S) Quantification of EDU analysis across each cohort of animals. Each error bar indicates standard deviation of percent EDU+ cells found within 3 fields for each animal.</p

Knockdown of PLXNA1 induced differentiation and impaired anchorage-independent growth of human ERMS cells.

<p>RD cells stained with myosin heavy chain (MF20) and DAPI following culture under differentiation conditions for 72 hrs. (A) Control siRNA. (B) <i>PLXNA1</i> smart-pool siRNA. (C) Control scrambled shRNA. (D) <i>PLXNA1</i> shRNA-1. DAPI, blue; MF20-positive cell, green. (E) Quantification of MF-20 immunofluorescence in siRNA and shRNA-knockdown RD cells. Asterisk indicates significant differences between gene knock- down and control cells (p<0.05). Error bars denote standard deviation. (F) Western analysis of <i>PLXNA1</i> shRNA stable knockdown; sc, scrambled control shRNA; 1, <i>PLXNA1</i> shRNA-1; 2, <i>PLXNA1</i> shRNA-2. A soft agar colony formation assay to assess PLXNA1 knockdown effects on anchorage-independent growth (G–I). (G) Control scrambled shRNA. (H) <i>PLXNA1</i> shRNA. (I) Quantification of colony formation assay results. Error bar indicates standard deviation from triplicate experiments.</p

Comparison of array CGH analyses in zebrafish and human ERMS.

<p>Zebrafish gene-containing, CNAs were compared to those identified in 26 primary human ERMS samples by Paulson et al. (2011). Genes selected for characterization in this study are in bold.</p

Knockdown of PLXNA1 impairs migration of human ERMS cells.

<p>Representative images of ERMS cells transfected with gene-specific siRNAs at 0 hr (A, control siRNA; C, <i>CCND2</i> siRNA; E, <i>HOXC6</i> siRNA; G, <i>PLXNA1</i> siRNA) and 22 hrs (B, control siRNA; D, <i>CCND2</i> siRNA; F, <i>HOXC6</i> siRNA; H, <i>PLXNA1</i> siRNA) following gap creation. Scale bar indicates 100 µm. (I) Quantification of data from wound healing assay. Each error bar indicates standard deviation across 5–6 independent replicates. (J) A Transwell migration assay was performed in RD cells that stably express either a control shRNA or two independent <i>PLXNA1</i> shRNAs. Migration was assessed after 24 hours. Each error bar indicates standard deviation across six fields at 200× magnification. Asterisks denote p<0. 05.</p

Array CGH reveals cancer-specific chromosomal abnormalities in zebrafish ERMS.

<p>(A) Summary of common gene-containing CNA gains (green) and losses (red) in 20 animals examined. Only recurrent CNAs found in ≥3 samples are shown. The height of each bar correlates with the frequency of each aberration. Detailed view of regional gains for <i>vegfa</i> on chromosome 4 (B), <i>ccnd2a</i> on chromosome 25 (C), <i>hoxc6a</i> on chromosome 23 (D), and <i>plxna1</i> on chromosome 6 (E). Y-axis denotes log2 ratio of the probes and X-axis denotes genomic coordinates.</p

The regulatory functions of NE1 locus.

<p>(A) The LTR region was determined using the Repeat Masker Track version 3.2.7 <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003404#pgen.1003404-Jurka1" target="_blank">[63]</a>. Dark red boxes indicate the location of amplicons for ChIP qPCR. TFBS-1 and TFBS-2 refers to the two predicted transcription factor binding sites that were predicted by the latest ENCODE project data release, available in UCSC Genome Browser for the hg19 assembly. The CNVR8163.1 deletion polymorphism is flanked by black vertical lines. The blue vertical lines indicate the approximate locations of SNPs that differentiate between NE1 and nonNE1 haplogroups and overlap the TFBS-1 and TFBS-2 transcription factor binding sites. The 8 SNPs that overlap with TFBS-2 are from 5′ to 3′, rs132525, rs4306795, rs4434085, rs5750701, rs35853418, rs6001308, rs6001309, and rs9622868) (B) Chromatin immunoprecipitation quantitative PCR (ChIP-qPCR) results across the NE1 locus for representative samples NA12155 and NA10851 that belong to NE1 and nonNE1 haplogroups, respectively. The locations of the amplified segments (P1–P6) are shown in dark red rectangles in (A). The positive control primers amplify a segment within <i>BCL6</i> gene on chromosome 3 that is known to have high H3K4me2 occupancy. The blue stars indicate significant differences in qPCR amplification between NE1 and nonNE1 haplotypes (p<0.01). The brown and blue arrows indicate qPCR primers that are closest to the predicted transcription binding sites (P1, P2, P3 for TFBS-1 and P7 for TFBS-2). Overall, our results demonstrate that H3K4me2 is enriched in NA12155 cells, which harbor the NE1 deletion as compared to NA10851 cells which do not have the deletion (data plotted represents the average of four replicate experiments ± Std. Error).</p

Ancient African origins of the NE1 haplogroup.

<p>(A) Models of scenarios that could lead to NE1 haplotypes observed in humans and Neandertals. The frequency of the NE1 haplogroup is depicted in red and the frequency of the nonNE1 haplogroup in yellow. The red corresponds to higher frequencies, whereas yellow corresponds to lower frequencies of the NE1 haplotypes in the population. The arrows represent the direction of possible admixture events. The left panel represents a model, under which the NE1 haplotypes admixed into Eurasian populations (Asn and Eur) after Human-Neandertal divergence. The second model, which is depicted in the central panel, is similar to the first model, except with the addition of more recent back migration of Eurasian NE1 haplotypes into Africa (Afr). The right panel shows the third model, under which the NE1 haplotypes among humans are explained by persistence of ancient African substructure. All these scenarios were based on the assumption that the NE1 haplotype occurs at high frequency or is fixed in the Neandertal population given that the available Neandertal sequences align well to the NE1 haplotype. (B) Geographical distribution of the NE1 haplogroup. We estimated the proportion of chromosomes that carry the CNVR8163.1 deletion from various sources described in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003404#s4" target="_blank"><i>Materials and Methods</i></a>. The dark red portion of each circle represents the frequency of the homozygous nonNE1 genotypes, the white represents the homozygous NE1 genotypes and the light red represents the frequency of heterozygote genotypes. Note the existence of the NE1 haplotypes (i.e., as heterozygotes, <i>light</i> red) among sub-Saharan African populations (e.g., LWK and ASW) as well as the high frequency of heterozygotes (<i>light red</i>) in the European populations. (C) The pairwise distances between the African (Afr) NE1 haplotypes, the Asian (Asn) NE1 haplotypes, and the European (Eur) NE1 haplotypes, calculated using phase 1 data from the 1000 Genomes Project. p-values were calculated by the Mann-Whitney test.</p

Selection acting on the NE1 locus.

<p>(A) Maximum likelihood tree based on select NE1 <i>(red)</i> and nonNE1 <i>(blue)</i> haplotypes, with the chimpanzee haplotype as an outgroup. The gray-box indicates the estimated interval for the Human-Neandertal divergence between 400,000–800,000 years ago <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003404#pgen.1003404-Eriksson1" target="_blank">[51]</a>. Note that the coalescence at this locus is extremely long and very unlikely to have evolved under neutral conditions as modeled here. (B) Comparison of <i>F_ST</i> and Tajima's <i>D</i> values of 10 kb intervals across the human genome. The red to dark blue gradient indicates decreased density of observed events at a given location in the graph. The NE1 locus, and other loci with similar profiles, are highlighted in white.</p