Episodic future thinking (EFT), an intervention involving mental simulation of future events, has been shown to reduce both delay discounting and cigarette self-administration. In the present study, we extended these findings by showing that EFT in a web-based sample of smokers reduces delay discounting and intensity of demand for cigarettes (ad libitum consumption) in a hypothetical purchase task. No effect was observed on elasticity of demand (sensitivity to price) or cigarette craving. We also explored whether demand characteristics (specifically, the "good-subject" effect) might be responsible for observed effects. EFT participants were significantly better able than control participants to discern the experimental hypothesis. However, EFT participants were not better than controls at identifying whether they had been assigned to the experimental group and, likewise, showed no differences in attitudes about the experiment and experimenter. Importantly, effects of EFT on delay discounting and demand remained significant even when controlling for measures of demand characteristics, indicating that EFT's effects are independent of participants' perceptions about the experiment.

Signaling pathways are a cornerstone of systems biology. Several databases store high-quality representations of these pathways that are amenable for automated analyses. Despite painstaking and manual curation, these databases remain incomplete. We present PATHLINKER, a new computational method to reconstruct the interactions in a signaling pathway of interest. PATHLINKER efficiently computes multiple short paths from the receptors to transcriptional regulators (TRs) in a pathway within a background protein interaction network. We use PATHLINKER to accurately reconstruct a comprehensive set of signaling pathways from the NetPath and KEGG databases. We show that PATHLINKER has higher precision and recall than several state-of-the-art algorithms, while also ensuring that the resulting network connects receptor proteins to TRs. PATHLINKER’s reconstruction of the Wnt pathway identified CFTR, an ABC class chloride ion channel transporter, as a novel intermediary that facilitates the signaling of Ryk to Dab2, which are known components of Wnt/β-catenin signaling. In HEK293 cells, we show that the Ryk–CFTR–Dab2 path is a novel amplifier of β-catenin signaling specifically in response to Wnt 1, 2, 3, and 3a of the 11 Wnts tested. PATHLINKER captures the structure of signaling pathways as represented in pathway databases better than existing methods. PATHLINKER’s success in reconstructing pathways from NetPath and KEGG databases point to its applicability for complementing manual curation of these databases. PATHLINKER may serve as a promising approach for prioritizing proteins and interactions for experimental study, as illustrated by its discovery of a novel pathway in Wnt/β-catenin signaling. Our supplementary website at http://bioinformatics.cs.vt.edu/~murali/supplements/2016-sys-bio-applications-pathlinker/ provides links to the PATHLINKER software, input datasets, PATHLINKER reconstructions of NetPath pathways, and links to interactive visualizations of these reconstructions on GraphSpace.

Knowing the quality of a protein structure model is important for its appropriate usage. We developed a model evaluation method to assess the absolute quality of a single protein model using only structural features with support vector machine regression. The method assigns an absolute quantitative score (i.e. GDT-TS) to a model by comparing its secondary structure, relative solvent accessibility, contact map, and beta sheet structure with their counterparts predicted from its primary sequence. We trained and tested the method on the CASP6 dataset using cross-validation. The correlation between predicted and true scores is 0.82. On the independent CASP7 dataset, the correlation averaged over 95 protein targets is 0.76; the average correlation for template-based and ab initio targets is 0.82 and 0.50, respectively. Furthermore, the predicted absolute quality scores can be used to rank models effectively. The average difference (or loss) between the scores of the top-ranked models and the best models is 5.70 on the CASP7 targets. This method performs favorably when compared with the other methods used on the same dataset. Moreover, the predicted absolute quality scores are comparable across models for different proteins. These features make the method a valuable tool for model quality assurance and ranking.

Protein contact map prediction is useful for protein folding rate prediction, model selection and 3D structure prediction. Here we describe NNcon, a fast and reliable contact map prediction server and software. NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008. Both NNcon server and software are available at http://casp.rnet.missouri.edu/nncon.html.

The ventricular-subventricular zone harbors neural stem cells (NSCs) that can differentiate into neurons, astrocytes, and oligodendrocytes. This process requires loss of stem cell properties and gain of characteristics associated with differentiated cells. miRNAs function as important drivers of this transition; miR-124, -128, and -137 are among the most relevant ones and have been shown to share commonalities and act as proneurogenic regulators. We conducted biological and genomic analyses to dissect their target repertoire during neurogenesis and tested the hypothesis that they act cooperatively to promote differentiation. To map their target genes, we transfected NSCs with antagomiRs and analyzed differences in their mRNA profile throughout differentiation with respect to controls. This strategy led to the identification of 910 targets for miR-124, 216 for miR-128, and 652 for miR-137. The target sets show extensive overlap. Inspection by gene ontology and network analysis indicated that transcription factors are a major component of these miRNAs target sets. Moreover, several of these transcription factors form a highly interconnected network. Sp1 was determined to be the main node of this network and was further investigated. Our data suggest that miR-124, -128, and -137 act synergistically to regulate Sp1 expression. Sp1 levels are dramatically reduced as cells differentiate and silencing of its expression reduced neuronal production and affected NSC viability and proliferation. In summary, our results show that miRNAs can act cooperatively and synergistically to regulate complex biological processes like neurogenesis and that transcription factors are heavily targeted to branch out their regulatory effect. STEM CELLS 2016;34:220-232 SIGNIFICANCE STATEMENTWe demonstrate here, perhaps for the first time, that miRNAs (miR-124, -128 and -137) can act cooperatively to regulate a complex biological problem such as neurogenesis. They do so by targeting overlapping sets of genes. Moreover, our genomic analyses determined that transcription factors are the main component of their target sets. Our results indicate additional associations between the three analyzed miRNA and the targeted TFs. Based on described ChIP analysis, we suggest that miR-124, -128 and -137 and their targeted TFs could act on overlapping and related target sets producing different functional outcomes that ultimately would influence the balance self-renewal/differentiation.

Evaluating the quality of protein structure models is important for selecting and using models. Here, we describe the MULTICOM series of model quality predictors which contains three predictors tested in the CASP8 experiments. We evaluated these predictors on 120 CASP8 targets. The average correlations between predicted and real GDT‐TS scores of the two semi‐clustering methods (MULTICOM and MULTICOM‐CLUSTER) and the one single‐model ab initio method (MULTICOM‐CMFR) are 0.90, 0.89, and 0.74, respectively; and their average difference (or GDT‐TS loss) between the global GDT‐TS scores of the top‐ranked models and the best models are 0.05, 0.06, and 0.07, respectively. The average correlation between predicted and real local quality scores of the semi‐clustering methods is above 0.64. Our results show that the novel semi‐clustering approach that compares a model with top ranked reference models can improve initial quality scores generated by the ab initio method and a simple meta approach. Proteins 2009. © 2009 Wiley‐Liss, Inc.

Aims Most cigarette smokers want to quit smoking and more than half make an attempt every year, but less than 10% remain abstinent for at least 6 months. Evidence-based tobacco use treatment improves the likelihood of quitting, but more than two-thirds of individuals relapse when provided even the most robust treatments. Identifying for whom treatment is effective will improve the success of our treatments and perhaps identify strategies for improving current approaches. Methods Two cohorts (training: N = 90, validation: N = 71) of cigarette smokers enrolled in group cognitive-behavioral therapy (CBT). Generalized estimating equations were used to identify baseline predictors of outcome, as defined by breath carbon monoxide and urine cotinine. Significant measures were entered as candidate variables to predict quit status. The resulting decision trees were used to predict cessation outcomes in a validation cohort. Results In the training cohort, the decision trees significantly improved on chance classification of smoking status following treatment and at 6-month follow-up. The first split of all decision trees, which was delay discounting, significantly improved on chance classification rates in both the training and validation cohort. Delay discounting emerged as the single best predictor of group CBT treatment response with an average baseline discount rate of ln(k) = −7.1, correctly predicting smoking status of 80% of participants at posttreatment and 81% of participants at follow-up. Conclusions This study provides a first step toward personalized care for smoking cessation though future work is needed to identify individuals that are likely to be successful in treatments beyond group CBT. Implications This study provides a first step toward personalized care for smoking cessation. Using a novel machine-learning approach, baseline measures of clinical and executive functioning are used to predict smoking cessation outcomes following group CBT. A decision point is recommended for the single best predictor of treatment outcomes, delay discounting, to inform future research or clinical practice in an effort to better allocate patients to treatments that are likely to work.

RNA-binding proteins (RBPs) and miRNAs are critical gene expression regulators that interact with one another in cooperative and antagonistic fashions. We identified Musashi1 (Msi1) and miR-137 as regulators of a molecular switch between self-renewal and differentiation. Msi1 and miR-137 have opposite expression patterns and functions, and Msi1 is repressed by miR-137. Msi1 is a stem-cell protein implicated in self-renewal while miR-137 functions as a proneuronal differentiation miRNA. In gliomas, miR-137 functions as a tumor suppressor while Msi1 is a prooncogenic factor. We suggest that the balance between Msi1 and miR-137 is a key determinant in cell fate decisions and disruption of this balance could contribute to neurodegenerative diseases and glioma development. Genomic analyses revealed that Msi1 and miR-137 share 141 target genes associated with differentiation, development, and morphogenesis. Initial results pointed out that these two regulators have an opposite impact on the expression of their target genes. Therefore, we propose an antagonistic model in which this network of shared targets could be either repressed by miR-137 or activated by Msi1, leading to different outcomes (self-renewal, proliferation, tumorigenesis).