Assessing the utility of thermodynamic features for microRNA target prediction under relaxed seed and no conservation requirements

Background

Many computational microRNA target prediction tools are focused on several key features, including complementarity to 5′seed of miRNAs and evolutionary conservation. While these features allow for successful target identification, not all miRNA target sites are conserved and adhere to canonical seed complementarity. Several studies have propagated the use of energy features of mRNA:miRNA duplexes as an alternative feature. However, different independent evaluations reported conflicting results on the reliability of energy-based predictions. Here, we reassess the usefulness of energy features for mammalian target prediction, aiming to relax or eliminate the need for perfect seed matches and conservation requirement.

Methodology/Principal Findings

We detect significant differences of energy features at experimentally supported human miRNA target sites and at genome-wide sites of AGO protein interaction. This trend is confirmed on datasets that assay the effect of miRNAs on mRNA and protein expression changes, and a simple linear regression model leads to significant correlation of predicted versus observed expression change. Compared to 6-mer seed matches as baseline, application of our energy-based model leads to ∼3–5-fold enrichment on highly down-regulated targets, and allows for prediction of strictly imperfect targets with enrichment above baseline.

Conclusions/Significance

In conclusion, our results indicate significant promise for energy-based miRNA target prediction that includes a broader range of targets without having to use conservation or impose stringent seed match rules.     

TargetSpy: a supervised machine learning approach for microRNA target prediction

Background  

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.     

A protein domain interaction interface database: InterPare

Background  

Most proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently.     

Selection of antisense oligonucleotides based on multiple predicted target mRNA structures

Background  

Local structures of target mRNAs play a significant role in determining the efficacies of antisense oligonucleotides (ODNs), but some structure-based target site selection methods are limited by uncertainties in RNA secondary structure prediction. If all the predicted structures of a given mRNA within a certain energy limit could be used simultaneously, target site selection would obviously be improved in both reliability and efficiency. In this study, some key problems in ODN target selection on the basis of multiple predicted target mRNA structures are systematically discussed.     

Fast prediction of RNA-RNA interaction

Background  

Regulatory antisense RNAs are a class of ncRNAs that regulate gene expression by prohibiting the translation of an mRNA by establishing stable interactions with a target sequence. There is great demand for efficient computational methods to predict the specific interaction between an ncRNA and its target mRNA(s). There are a number of algorithms in the literature which can predict a variety of such interactions - unfortunately at a very high computational cost. Although some existing target prediction approaches are much faster, they are specialized for interactions with a single binding site.     

Prenatal Exposure to TCDD Triggers Significant Modulation of microRNA Expression Profile in the Thymus That Affects Consequent Gene Expression

Background

MicroRNAs (miRs) are a class of small RNAs that regulate gene expression. There are over 700 miRs encoded in the mouse genome and modulate most of the cellular pathways and functions by controlling gene expression. However, there is not much known about the pathophysiological role of miRs. TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin), an environmental contaminant is well known to induce severe toxicity (acute and chronic) with long-term effects. Also, in utero exposure of fetus to TCDD has been shown to cause thymic atrophy and alterations in T cell differentiation. It is also relevant to understand “the fetal basis of adult disease” hypothesis, which proposes that prenatal exposure to certain forms of nutritional and environmental stress can cause increased susceptibility to clinical disorders later in life. In the current study, therefore, we investigated the effects of prenatal exposure to TCDD on miR profile in fetal thymocytes and searched for their possible role in causing thymic atrophy and alterations in the expression of apoptotic genes.

Methodology/Principal Findings

miR arrays of fetal thymocytes post exposure to TCDD and vehicle were performed. Of the 608 mouse miRs screened, 78 miRs were altered more than 1.5 fold and 28 miRs were changed more than 2 fold in fetal thymocytes post-TCDD exposure when compared to vehicle controls. We validated the expression of several of the miRs using RT-PCR. Furthermore, several of the miRs that were downregulated contained highly complementary sequence to the 3′-UTR region of AhR, CYP1A1, Fas and FasL. Also, the Ingenuity Pathway Analysis software and database was used to analyze the 78 miRs that exhibited significant expression changes and revealed that as many as 15 pathways may be affected.

Conclusions/Significance

These studies revealed that TCDD-mediated alterations in miR expression may be involved in the regulation of its toxicity including cancer, hepatic injury, apoptosis, and cellular development.     

Multiplex real‐time PCR assays for detection of four seedborne spinach pathogens

Aims

To develop multiplex TaqMan real‐time PCR assays for detection of spinach seedborne pathogens that cause economically important diseases on spinach.

Methods and Results

Primers and probes were designed from conserved sequences of the internal transcribed spacer (for Peronospora farinosa f. sp. spinaciae and Stemphylium botryosum), the intergenic spacer (for Verticillium dahliae) and the elongation factor 1 alpha (for Cladosporium variabile) regions of DNA. The TaqMan assays were tested on DNA extracted from numerous isolates of the four target pathogens, as well as a wide range of nontarget, related fungi or oomycetes and numerous saprophytes commonly found on spinach seed. Multiplex real‐time PCR assays were evaluated by detecting two or three target pathogens simultaneously. Singular and multiplex real‐time PCR assays were also applied to DNA extracted from bulked seed and single spinach seed.

Conclusions

The real‐time PCR assays were species‐specific and sensitive. Singular or multiplex real‐time PCR assays could detect target pathogens from both bulked seed samples as well as single spinach seed.

Significance and Impact of the Study

The freeze‐blotter assay that is currently routinely used in the spinach seed industry to detect and quantify three fungal seedborne pathogens of spinach (C. variabile, S. botryosum and V. dahliae) is quite laborious and takes several weeks to process. The real‐time PCR assays developed in this study are more sensitive and can be completed in a single day. As the assays can be applied easily for routine seed inspections, these tools could be very useful to the spinach seed industry.