Phyto-gene therapy using antisense oligonucleotides to control cereal fungal disease by silencing virulence factors and their regulators

With increasing concerns regarding food security, alternative solutions are required for disease control in crops, including those caused by fungal pathogens. Antisense single stranded short oligodeoxynucleotides (ASO) based gene therapy is widely used in medicine but is still emerging in plant sciences. The ASO gene silencing approach using phosphorothioate modified oligodeoxynucleotides (asPTOs) delivered to excised barley leaves was first devised as a tool for in planta transient host induced gene silencing (HIGS) to query the virulence role of genes from the biotrophic fungal pathogen, Blumeria graminis f.sp. hordei (Bgh), the causal agent of barley powdery mildew. Following this, our project aimed at exploiting the HIGS approach for discovering new key players for virulence of Bgh and some of the major wheat pathogens, B. graminis f.sp. tritici (Bgt) and Fusarium graminearum, the causal agent of Fusarium head blight. The ASO gene silencing approach was also evaluated for its suitability to protect wheat against fungi by targeting host susceptibility genes. AsPTOs to silence vital Bgh genes (actin, GAPDH, 2-Glycosyl transferase) successfully reduced powdery mildew infection in several barley cultivars. Similarly, silencing the metallo-protease-like effector BEC1019 impacted on Bgh and Bgt virulence in barley and wheat respectively. Following promoter sequence analysis of Bgh effectors expressed in haustoria, the HIGS approach allowed to confirm the implication of ZAP1 and PacC transcription factors in regulating BEC1019 and BEC1011 effector expression, while affecting Bgh virulence. To adapt ASO based gene silencing for disease control, in planta gene silencing of F. graminearum known virulence genes was attempted but with no convincing impact. However, asPTOs to silence BEC1011 were delivered into whole barley seedlings by root uptake resulting in reduced powdery mildew infection. This suggests that asPTOs based HIGS could be further investigated as a strategy to control fungal diseases in crops. <br/

Competing Endogenous RNA: The Key to Posttranscriptional Regulation

Competing endogenous RNA, ceRNA, vie with messenger RNAs (mRNAs) for microRNAs (miRNAs) with shared miRNAs responses elements (MREs) and act as modulator of miRNA by influencing the available level of miRNA. It has recently been discovered that, apart from protein-coding ceRNAs, pseudogenes, long noncoding RNAs (lncRNAs), and circular RNAs act as miRNA “sponges” by sharing common MRE, inhibiting normal miRNA targeting activity on mRNA. These MRE sharing elements form the posttranscriptional ceRNA network to regulate mRNA expression. ceRNAs are widely implicated in many biological processes. Recent studies have identified ceRNAs associated with a number of diseases including cancer. This brief review focuses on the molecular mechanism of ceRNA as part of the complex post-transcriptional regulatory circuit in cell and the impact of ceRNAs in development and disease

Cancer-Specific Immune Prognostic Signature in Solid Tumors and Its Relation to Immune Checkpoint Therapies

To elucidate the role of immune cell infiltration as a prognostic signature in solid tumors, we analyzed immune-function-related genes from four publicly available single-cell RNA-Seq data sets and twenty bulk tumor RNA-Seq data sets from The Cancer Genome Atlas (TCGA). Unsupervised clustering of pan-cancer transcriptomic signature showed two major immune function types: one related to NK-, T-, and B-cell functions and the other related to monocyte, macrophage, dendritic cell, and Toll-like receptor functions. Kaplan&ndash;Meier analysis showed differential prognosis of these two groups, dependent on the cancer type. Our analysis of TCGA solid tumors with an elastic net model identified 155 genes associated with disease-free survival in different tumor types with varied influence across different cancer types. With this gene set, we computed cancer-specific prognostic immune score models for individual cancer types that predicted disease-free and overall survival. Validation of our model on available published data of immune checkpoint blockade therapies on melanoma, kidney renal cell carcinoma, non-small cell lung cancer, gastric cancer and bladder cancer confirmed that cancer-specific higher immune scores are associated with response to immunotherapy. Our analysis provides a comprehensive map of cancer-specific immune-related prognostic gene sets that are associated with immunotherapy response

lnCeDB: database of human long noncoding RNA acting as competing endogenous RNA.

Long noncoding RNA (lncRNA) influences post-transcriptional regulation by interfering with the microRNA (miRNA) pathways, acting as competing endogenous RNA (ceRNA). These lncRNAs have miRNA responsive elements (MRE) in them, and control endogenous miRNAs available for binding with their target mRNAs, thus reducing the repression of these mRNAs. lnCeDB provides a database of human lncRNAs (from GENCODE 19 version) that can potentially act as ceRNAs. The putative mRNA targets of human miRNAs and the targets mapped to AGO clipped regions are collected from TargetScan and StarBase respectively. The lncRNA targets of human miRNAs (up to GENCODE 11) are downloaded from miRCode database. miRNA targets on the rest of the GENCODE 19 lncRNAs are predicted by our algorithm for finding seed-matched target sites. These putative miRNA-lncRNA interactions are mapped to the Ago interacting regions within lncRNAs. To find out the likelihood of an lncRNA-mRNA pair for actually being ceRNA we take recourse to two methods. First, a ceRNA score is calculated from the ratio of the number of shared MREs between the pair with the total number of MREs of the individual candidate gene. Second, the P-value for each ceRNA pair is determined by hypergeometric test using the number of shared miRNAs between the ceRNA pair against the number of miRNAs interacting with the individual RNAs. Typically, in a pair of RNAs being targeted by common miRNA(s), there should be a correlation of expression so that the increase in level of one ceRNA results in the increased level of the other ceRNA. Near-equimolar concentration of the competing RNAs is associated with more profound ceRNA effect. In lnCeDB one can not only browse for lncRNA-mRNA pairs having common targeting miRNAs, but also compare the expression of the pair in 22 human tissues to estimate the chances of the pair for actually being ceRNAs.Downloadable freely from http://gyanxet-beta.com/lncedb/

SeedSeq: Off-Target Transcriptome Database

Detection of potential cross-reaction between a short oligonucleotide sequence and a longer (unintended) sequence is crucial for many biological applications, such as high content screening (HCS), microarray nucleotide probes, or short interfering RNAs (siRNAs). However, owing to a tolerance for mismatches and gaps in base-pairing with target transcripts, siRNAs could have up to hundreds of potential target sequences in a genome, and some small RNAs in mammalian systems have been shown to affect the levels of many messenger RNAs (off-targets) besides their intended target transcripts (on-targets). The reference sequence (RefSeq) collection aims to provide a comprehensive, integrated, nonredundant, well-annotated set of sequences, including mRNA transcripts. We performed a detailed off-target analysis of three most commonly used kinome siRNA libraries based on the latest RefSeq version. To simplify the access to off-target transcripts, we created a SeedSeq database, a new unique format to store off-target information

SL-BioDP: Multi-Cancer Interactive Tool for Prediction of Synthetic Lethality and Response to Cancer Treatment

Synthetic lethality exploits the phenomenon that a mutation in a cancer gene is often associated with new vulnerability which can be uniquely targeted therapeutically, leading to a significant increase in favorable outcome. DNA damage and survival pathways are among the most commonly mutated networks in human cancers. Recent data suggest that synthetic lethal interactions between a tumor defect and a DNA repair pathway can be used to preferentially kill tumor cells. We recently published a method, DiscoverSL, using multi-omic cancer data, that can predict synthetic lethal interactions of potential clinical relevance. Here, we apply the generality of our models in a comprehensive web tool called Synthetic Lethality Bio Discovery Portal (SL-BioDP) and extend the cancer types to 18 cancer genome atlas cohorts. SL-BioDP enables a data-driven computational approach to predict synthetic lethal interactions from hallmark cancer pathways by mining cancer&rsquo;s genomic and chemical interactions. Our tool provides queries and visualizations for exploring potentially targetable synthetic lethal interactions, shows Kaplan&ndash;Meier plots of clinical relevance, and provides in silico validation using short hairpin RNA (shRNA) and drug efficacy data. Our method would thus shed light on mechanisms of synthetic lethal interactions and lead to the discovery of novel anticancer drugs

Glioma-BioDP: database for visualization of molecular profiles to improve prognosis of brain cancer

Abstract Cancer researchers often seek user-friendly interactive tools for validation, exploration, analysis, and visualization of molecular profiles in cancer patient samples. To aid researchers working on the both low- and high-grade gliomas, we developed Glioma-BioDP, a web tool for exploration and visualization of RNA and protein expression profiles of interest in these tumor types. Glioma-BioDP is user friendly application that include expression data from both the low- and high-grade glioma patient samples from The Cancer Genome Atlas and enabled querying by mRNA, microRNA, and protein level expression data from Illumina HiSeq and RPPA platforms respectively. Glioma-BioDP provides advance query interface and enables users to explore the association of genes, proteins, and miRNA expression with molecular and/or histological subtypes of gliomas, surgical resection status and survival. The prognostic significance and visualization of the selected expression profiles can be explored using interactive utilities provided. This tool may also enable validation and generation of new hypotheses of novel therapies impacting gliomas that aid in personalization of treatment for optimum outcomes