Positive and negative regulation ofCAR1 Expression inSaccharomyces cerevisiae

Summary We localized the chromosomal targets of several of the regulatory controls of expression of theCAR1 gene. Fusion tolacZ of several fragments of the 5′ non-coding region showed that induction ofCAR1 by arginine is positively regulated by the products of theARGR genes. The target lies upstream of another site where repression by the CARGRI molecule occurs. The latter control is not specific to arginine catabolism since it also affectsCYC-1 and indeed does not appear to involve arginine. The primary target of the two other regulatory allelesCARGRII andCARGRIII is not situated in the 5′ non-coding region. Deletion analysis supports the fusion data and confirms the order of the regulatory regions: 5′—nitrogen catabolite repression—activation by arginine—CARGRI-mediated repression—CAR1.     

A molecular dynamics study of a miRNA:mRNA interaction

In this paper we present a methodology to evaluate the binding free energy of a miRNA:mRNA complex through molecular dynamics (MD)–thermodynamic integration (TI) simulations. We applied our method to the Caenorhabditis elegans let-7 miRNA:lin-41 mRNA complex—a validated miRNA:mRNA interaction—in order to estimate the energetic stability of the structure. To make the miRNA:mRNA simulation possible and realistic, the methodology introduces specific solutions to overcome some of the general challenges of nucleic acid simulations and binding free energy computations that have been discussed widely in many previous research reports. The main features of the proposed methodology are: (1) positioning of the restraints imposed on the simulations in order to guarantee complex stability; (2) optimal sampling of the phase space to achieve satisfactory accuracy in the binding energy value; (3) determination of a suitable trade-off between computational costs and accuracy of binding free energy computation by the assessment of the scalability characteristics of the parallel simulations required for the TI. The experiments carried out demonstrate that MD simulations are a viable strategy for the study of miRNA binding characteristics, opening the way to the development of new computational target prediction methods based on three-dimensional structure information.     

Mutation Pattern Variation Among Regions of the Primate Genome

We sequenced three argininosuccinate-synthetase-processed pseudogenes (ΨAS-A1, ΨAS-A3, ΨAS-3) and their noncoding flanking sequences in human, orangutan, baboon, and colobus. Our data showed that these pseudogenes were incorporated into the genome of the Old World monkeys after the divergence of the Old World and New World monkey lineages. These pseudogene flanking regions show variable mutation rates and patterns. The variation in the G/C to A/T mutation rate (u) can account for the unequal GC contents at equilibrium: 34.9, 36.9, and 41.7% in the pseudogene ΨAS-A1, ΨAS-A3, and ΨAS-3 flanking regions, respectively. The A/T to G/C mutation rate (v) seems stable and the u/v ratios equal 1.9, 1.7, and 1.4 in the flanking regions of ΨAS-A1, ΨAS-A3, and ΨAS-3, respectively. These ``regional' variations of the mutation rate affect the evolution of the pseudogenes, too. The ratio u/v being greater than 1.0 in each case, the overall mutation rate in the GC-rich pseudogenes is, as expected, higher than in their GC-poor flanking regions. Moreover, a ``sequence effect' has been found. In the three cases examined u and v are higher (at least 20%) in the pseudogene than in its flanking region—i.e., the pseudogene appears as mutation ``hot' spots embedded in ``cold' regions. This observation could be partly linked to the fact that the pseudogene flanking regions are long-standing unconstrained DNA sequences, whereas the pseudogenes were relieved of selection on their coding functions only around 30–40 million years ago. We suspect that relatively more mutable sites maintained unchanged during the evolution of the argininosuccinate gene are able to change in the pseudogenes, such sites being eliminated or rare in the flanking regions which have been void of strong selective constraints over a much longer period. Our results shed light on (1) the multiplicity of factors that tune the spontaneous mutation rate and (2) the impact of the genomic position of a sequence on its evolution. Received: 10 February 1997 / Accepted: 21 April 1997     

Compositional Correlations in the Chicken Genome

This paper analyses the compositional correlations that hold in the chicken genome. Significant linear correlations were found among the regions studied—coding sequences (and their first, second, and third codon positions), flanking regions (5′ and 3′), and introns—as is the case in the human genome. We found that these compositional correlations are not limited to global GC levels but even extend to individual bases. Furthermore, an analysis of 1037 coding sequences has confirmed a correlation among GC₃, GC₂, and GC₁. The implications of these results are discussed. Received: 9 December 1998 / Accepted: 18 April 1999     

Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network

Background

Recent studies have shown that the regulatory effect of microRNAs can be investigated by examining expression changes of their target genes. Given this, it is useful to define an overall metric of regulatory effect for a specific microRNA and see how this changes across different conditions.

Results

Here, we define a regulatory effect score (RE-score) to measure the inhibitory effect of a microRNA in a sample, essentially the average difference in expression of its targets versus non-targets. Then we compare the RE-scores of various microRNAs between two breast cancer subtypes: estrogen receptor positive (ER⁺) and negative (ER^-). We applied this approach to five microarray breast cancer datasets and found that the expression of target genes of most microRNAs was more repressed in ER^- than ER⁺; that is, microRNAs appear to have higher RE-scores in ER^- breast cancer. These results are robust to the microRNA target prediction method. To interpret these findings, we analyzed the level of microRNA expression in previous studies and found that higher microRNA expression was not always accompanied by higher inhibitory effects. However, several key microRNA processing genes, especially Ago2 and Dicer, were differentially expressed between ER^- and ER⁺ breast cancer, which may explain the different regulatory effects of microRNAs in these two breast cancer subtypes.

Conclusions

The RE-score is a promising indicator to measure microRNAs' inhibitory effects. Most microRNAs exhibit higher RE-scores in ER^- than in ER⁺ samples, suggesting that they have stronger inhibitory effects in ER^- breast cancers.     

The Characterization of microRNA-Mediated Gene Regulation as Impacted by Both Target Site Location and Seed Match Type

MicroRNAs (miRNAs) are small RNA molecules that play important roles in gene regulation and translational repression. The mechanisms that facilitate miRNA target binding and recognition have been extensively studied in recent years. However, it is still not well known how the miRNA regulation is affected by the location and the flanking sequences of miRNA target sites. In this study, we systematically quantify the contribution of a wide spectrum of target sites on miRNA-mediated gene expression regulation. Our study investigates target sites located in four different gene regions, including 3'' untranslated regions, coding sequences, 5′ untranslated regions and promoter regions. We have also introduced four additional non-canonical types of seed matches beyond the canonical seed matches, and included them in our study. Computational analysis of quantitative proteomic data has demonstrated that target sites located in different regions impact the miRNA-mediated repression differently but synergistically. In addition, we have shown the synergistic effects among non-canonical seed matches and canonical ones that enhance the miRNA regulatory effects. Further systematic analysis on the site accessibility near the target regions and the secondary structure of the mRNA sequences have demonstrated substantial variations among target sites of different locations and of different types of seed matches, suggesting the mRNA secondary structure could explain some of the difference in the miRNA regulatory effects impacted by these different target sites. Our study implies miRNAs might regulate their targets under different mechanisms when target sites vary in both their locations and the types of seed matches they contain.     

Prediction of personalized microRNA activity

MicroRNAs have been known to regulate almost all physiological and pathological processes by suppressing their target genes. In humans, more than 1000 microRNAs have been identified, each of which targets dozens or even hundreds of genes. Facing this huge repertoire of microRNA targeting, it is important to identify which microRNAs are active, i.e., down-regulating their targets, in specific physiological or pathological conditions. Predicting active microRNAs is different from predicting microRNA targets because the authentic target genes of a microRNA are often not directly and solely regulated by that microRNA, leading to inconsistent expression changes between the microRNA and its true targets. Several computational programs have been proposed to predict the activity of a microRNA from the expressions of its target genes. These programs performed well when being applied on the expression data obtained from distinct tissue types or from experiments that transfect a microRNA into cells (i.e., non-physiological). But the performance of microRNA activity prediction is not clear on the expression data from the same tissue type in two physiological conditions, e.g., liver tissues from cancer patients and healthy people. In this work, we evaluate the performance of two microRNA activity prediction programs using seven expression data sets, all of which compare samples in two physiological conditions, as well as propose a new approach that predicts microRNA activity with an accuracy of over 80%. Unlike current methods, which predict active microRNAs by comparing two groups of samples, e.g., tumor versus normal, our new approach compares each diseased sample with all the samples in the control group. In other words, it can predict the microRNA activity of a person. In this work, this new application is named to predict “personalized microRNA activity”.     

mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer

Background

Recent studies have shown that the regulatory effect of microRNAs can be investigated by examining expression changes of their target genes. Given this, it is useful to define an overall metric of regulatory effect for a specific microRNA and see how this changes across different conditions.

Results

Here, we define a regulatory effect score (RE-score) to measure the inhibitory effect of a microRNA in a sample, essentially the average difference in expression of its targets versus non-targets. Then we compare the RE-scores of various microRNAs between two breast cancer subtypes: estrogen receptor positive (ER⁺) and negative (ER^-). We applied this approach to five microarray breast cancer datasets and found that the expression of target genes of most microRNAs was more repressed in ER^- than ER⁺; that is, microRNAs appear to have higher RE-scores in ER^- breast cancer. These results are robust to the microRNA target prediction method. To interpret these findings, we analyzed the level of microRNA expression in previous studies and found that higher microRNA expression was not always accompanied by higher inhibitory effects. However, several key microRNA processing genes, especially Ago2 and Dicer, were differentially expressed between ER^- and ER⁺ breast cancer, which may explain the different regulatory effects of microRNAs in these two breast cancer subtypes.

Conclusions

The RE-score is a promising indicator to measure microRNAs'' inhibitory effects. Most microRNAs exhibit higher RE-scores in ER^- than in ER⁺ samples, suggesting that they have stronger inhibitory effects in ER^- breast cancers.     

鸡6个功能基因microRNA靶标区域SNP的生物信息学预测

GDF-8、IGF-I、IGF-III、IGF2R、IGFBP2和GHR是鸡的重要经济性状候选基因。利用miRanda和Targetscan软件预测6个基因3′UTR潜在的microRNA靶标, 并发掘靶标区域SNP位点。结果表明: 在6个基因的26个microRNA靶标区域, 共检测到125个SNP位点, 在靶标及其5′和3′邻接等长侧翼区分别检测到47个、44个和35个SNPs位点, 其中12个SNP定位于靶标种子序列互补区。种子序列互补区及其3′侧翼区的SNP位点可能会影响microRNA的调控, 导致家禽的表型变异     

Large-Scale Screens of miRNA-mRNA Interactions Unveiled That the 3′UTR of a Gene Is Targeted by Multiple miRNAs

Animal microRNA (miRNA) target prediction is still a challenge, although many prediction programs have been exploited. MiRNAs exert their function through partially binding the messenger RNAs (mRNAs; likely at 3′ untranslated regions [3′UTRs]), which makes it possible to detect the miRNA-mRNA interactions in vitro by co-transfection of miRNA and a luciferase reporter gene containing the target mRNA fragment into mammalian cells under a dual-luciferase assay system. Here, we constructed a human miRNA expression library and used a dual-luciferase assay system to perform large-scale screens of interactions between miRNAs and the 3′UTRs of seven genes, which included more than 3,000 interactions with triplicate experiments for each interaction. The screening results showed that the 3′UTR of one gene can be targeted by multiple miRNAs. Among the prediction algorithms, a Bayesian phylogenetic miRNA target identification algorithm and a support vector machine (SVM) presented a relatively better performance (27% for EIMMo and 24.7% for miRDB) against the average precision (17.3%) of the nine prediction programs used here. Additionally, we noticed that a relatively high conservation level was shown at the miRNA 3′ end targeted regions, as well as the 5′ end (seed region) binding sites.     

Cumulative energy demand for selected renewable energy technologies

Calculation of Cumulative Energy Demand (CED) of various energy systems and the computation of their Energy Yield Ratio (EYR) suggests that one single renewable energy technology cannot be said to be the best. Due to the difference in availability of renewable energy sources, their suitability varies from place to place. Wind energy converters, solar water heating systems and photovoltaic systems have been analysed for different types of locations. Comparing the general bandwidth of performance of these technologies, however, the wind energy converters tend to be better, followed by solar water heating systems and photovoltaic systems. Since a major part of the methodology of findingCED is very close to that of life cycle assessment and also because of the dominance of environmental impacts caused by the energy demand in the entire life cycle of any product or system, it is suggested that theCED can be used as an indicator of environmental impacts, especially in the case of power producing systems. Keywords: Cumulative energy demand; life cycle assessment; energy yield ratio; photovoltaics; solar water heating; wind energy Abbreviations: CED — Cumulative Energy Demand; EYR — Energy Yield Ratio; LCA — Life Cycle Assessment; Photovoltaics — PV; WEC — Wind Energy Converters     

Handling Permutation in Sequence Comparison: Genome-Wide Enhancer Prediction in Vertebrates by a Novel Non-Linear Alignment Scoring Principle

Enhancers have been described to evolve by permutation without changing function. This has posed the problem of how to predict enhancer elements that are hidden from alignment-based approaches due to the loss of co-linearity. Alignment-free algorithms have been proposed as one possible solution. However, this approach is hampered by several problems inherent to its underlying working principle. Here we present a new approach, which combines the power of alignment and alignment-free techniques into one algorithm. It allows the prediction of enhancers based on the query and target sequence only, no matter whether the regulatory logic is co-linear or reshuffled. To test our novel approach, we employ it for the prediction of enhancers across the evolutionary distance of ~450Myr between human and medaka. We demonstrate its efficacy by subsequent in vivo validation resulting in 82% (9/11) of the predicted medaka regions showing reporter activity. These include five candidates with partially co-linear and four with reshuffled motif patterns. Orthology in flanking genes and conservation of the detected co-linear motifs indicates that those candidates are likely functionally equivalent enhancers. In sum, our results demonstrate that the proposed principle successfully predicts mutated as well as permuted enhancer regions at an encouragingly high rate.