Investigating the effects of the aging brain on real tool use performance-an fMRI study.

Introduction: Healthy aging affects several domains of cognitive and motor performance and is further associated with multiple structural and functional neural reorganization patterns. However, gap of knowledge exists, referring to the impact of these age-related alterations on the neural basis of tool use-an important, complex action involved in everyday life throughout the entire lifespan. The current fMRI study aims to investigate age-related changes of neural correlates involved in planning and executing a complex object manipulation task, further providing a better understanding of impaired tool use performance in apraxia patients. Methods: A balanced number of sixteen older and younger healthy adults repeatedly manipulated everyday tools in an event-related Go-No-Go fMRI paradigm. Results: Our data indicates that the left-lateralized network, including widely distributed frontal, temporal, parietal and occipital regions, involved in tool use performance is not subjected to age-related functional reorganization processes. However, age-related changes regarding the applied strategical procedure can be detected, indicating stronger investment into the planning, preparatory phase of such an action in older participants.

Transcription Factor-Centric Approach to Identify Non-Recurring Putative Regulatory Drivers in Cancer.

Recent efforts to sequence the genomes of thousands of matched normal-tumor samples have led to the identification of millions of somatic mutations, the majority of which are non-coding. Most of these mutations are believed to be passengers, but a small number of non-coding mutations could contribute to tumor initiation or progression, e.g. by leading to dysregulation of gene expression. Efforts to identify putative regulatory drivers rely primarily on information about the recurrence of mutations across tumor samples. However, in regulatory regions of the genome, individual mutations are rarely seen in more than one donor. Instead of using recurrence information, here we present a method to identify putative regulatory driver mutations based on the magnitude of their effects on transcription factor-DNA binding. For each gene, we integrate the effects of mutations across all its regulatory regions, and we ask whether these effects are larger than expected by chance, given the mutation spectra observed in regulatory DNA in the cohort of interest. We applied our approach to analyze mutations in a liver cancer data set with ample somatic mutation and gene expression data available. By combining the effects of mutations across all regulatory regions of each gene, we identified dozens of genes whose regulation in tumor cells is likely to be significantly perturbed by non-coding mutations. Overall, our results show that focusing on the functional effects of non-coding mutations, rather than their recurrence, has the potential to identify putative regulatory drivers and the genes they dysregulate in tumor cells.

QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants.

Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs, a myriad of DNA-binding models are available and could be used to predict the effects of DNA mutations on TF binding. However, information on the quality of these models is scarce, making it hard to evaluate the statistical significance of predicted binding changes. Here, we present QBiC-Pred, a web server for predicting quantitative TF binding changes due to nucleotide variants. QBiC-Pred uses regression models of TF binding specificity trained on high-throughput in vitro data. The training is done using ordinary least squares (OLS), and we leverage distributional results associated with OLS estimation to compute, for each predicted change in TF binding, a P-value reflecting our confidence in the predicted effect. We show that OLS models are accurate in predicting the effects of mutations on TF binding in vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning models of specificity. QBiC-Pred takes as input mutation datasets in several formats, and it allows post-processing of the results through a user-friendly web interface. QBiC-Pred is freely available at http://qbic.genome.duke.edu.

PEDF protects human retinal pigment epithelial cells against oxidative stress via upregulation of UCP2 expression.

To investigate the protective function of pigment epithelium­derived factor (PEDF) against oxidative stress (OS) in ARPE­19 cells, ARPE­19 cells were divided into different OS groups and treated with various concentrations of H2O2 (0, 75, 150 and 200 µmol/l) for 24 h. To establish the protective group, 200 ng/ml of PEDF was administered to ARPE­19 cells. Cell Counting Kit­8 assays and cell growth curve experiments were performed to determine levels of cell viability; lactate dehydrogenase and propidium iodide (PI) staining assays were also performed. The expression levels of genes associated with apoptosis as well as uncoupling protein 2 (UCP2) were detected by reverse transcription­quantitative, or semi­quantitative polymerase chain reaction. Furthermore, an OS injury animal model was established in both C57BL/6 and BALB/c mice via injection of 5 µg of PEDF in the vitreous cavity and subsequent injection of 150 µM H2O2 following a 24 h time interval. Hematoxylin and eosin (H&E) staining, as well as UCP2 immunofluorescent labeling were also performed. One­way analysis of variance was used to determine statistically significant differences, followed by multiple comparison analysis using the Newman Keuls method. The results of cell viability assays demonstrated that the numbers of apoptotic cells were increased following treatment with H2O2 in a dose­dependent manner; however, this effect was reversed following treatment with PEDF. The expression levels of caspase 3 and B cell lymphoma (Bcl2) associated X genes associated with apoptosis were inhibited, whereas levels of the anti­apoptotic gene Bcl2 were enhanced following treatment with PEDF in different passages of ARPE­19 cells. Significant differences were demonstrated in the levels of UCP2 gene expression between the PEDF+ H2O2 treated group and cells treated with H2O2 alone. Labeling of the UCP2 detector in the confocal images demonstrated decreased UCP2 protein staining in the retinal pigment epithelium (RPE) cells and RPE layers following H2O2 injury; however, this effect was inhibited following treatment with PEDF. H&E staining was performed to investigate the thickness of the RPE layers, and the results revealed that thicknesses were significantly increased in sections treated with PEDF during OS, due to increased numbers of RPE cells. Furthermore, PEDF was demonstrated to increase UCP2 gene expression in ARPE­19 cells and animal RPE layers under OS, which suggested that PEDF may protect RPE cells and tissues during oxidative injury.

Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding.

Paralogous transcription factors (TFs) are oftentimes reported to have identical DNA-binding motifs, despite the fact that they perform distinct regulatory functions. Differential genomic targeting by paralogous TFs is generally assumed to be due to interactions with protein co-factors or the chromatin environment. Using a computational-experimental framework called iMADS (integrative modeling and analysis of differential specificity), we show that, contrary to previous assumptions, paralogous TFs bind differently to genomic target sites even in vitro. We used iMADS to quantify, model, and analyze specificity differences between 11 TFs from 4 protein families. We found that paralogous TFs have diverged mainly at medium- and low-affinity sites, which are poorly captured by current motif models. We identify sequence and shape features differentially preferred by paralogous TFs, and we show that the intrinsic differences in specificity among paralogous TFs contribute to their differential in vivo binding. Thus, our study represents a step forward in deciphering the molecular mechanisms of differential specificity in TF families.

Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding.

Many recent studies have emphasized the importance of genetic variants and mutations in cancer and other complex human diseases. The overwhelming majority of these variants occur in non-coding portions of the genome, where they can have a functional impact by disrupting regulatory interactions between transcription factors (TFs) and DNA. Here, we present a method for assessing the impact of non-coding mutations on TF-DNA interactions, based on regression models of DNA-binding specificity trained on high-throughput in vitro data. We use ordinary least squares (OLS) to estimate the parameters of the binding model for each TF, and we show that our predictions of TF-binding changes due to DNA mutations correlate well with measured changes in gene expression. In addition, by leveraging distributional results associated with OLS estimation, for each predicted change in TF binding we also compute a normalized score (z-score) and a significance value (p-value) reflecting our confidence that the mutation affects TF binding. We use this approach to analyze a large set of pathogenic non-coding variants, and we show that these variants lead to significant differences in TF binding between alleles, compared to a control set of common variants. Thus, our results indicate that there is a strong regulatory component to the pathogenic non-coding variants identified thus far.

Inhibitory effect of CD147/HAb18 monoclonal antibody on cartilage erosion and synovitis in the SCID mouse model for rheumatoid arthritis.

OBJECTIVE: To explore the therapeutic potential of CD147/HAb18 mAb in the treatment of RA in severe combined immunodeficiency (SCID) mice engrafted with human cartilage and rheumatoid synovium tissue (SCID-HuRAg). METHODS: SCID-HuRAg mice were treated separately with CD147/HAb18 mAb, anti-TNF-alpha mAb or a combination of both. The mice in control group were treated with anti-Japanese encephalitis virus mAb. The volume of engrafts was measured and the number of inflammatory cells and cartilage erosion score were examined. Expression of MMP-2, -3 and -9 was determined by immunohistochemistry. Human inflammatory cytokine levels in mouse sera were assessed using cytometric bead array kit. RESULTS: The volume of engrafts decreased significantly in SCID-HuRAg mice treated separately with anti-CD147 mAb or anti-TNF-alpha mAb, and in the mice treated with anti-CD147 mAb plus anti-TNF-alpha mAb (P < 0.05). Significant reduction was observed in cartilage erosion score in anti-CD147 treatment group and combined treatment group (P < 0.05). Immunohistochemical analysis showed that expression of MMP-2, -3 and -9 was lower in the anti-CD147 treatment group and combined treatment group than in the control mAb group (P < 0.05). Moreover, the level of TNF-alpha, IL-6 and -8 in CD147 mAb group showed a significant decrease compared with that of the control mAb group (P < 0.05). CONCLUSIONS: CD147/HAb18 mAb can reduce cartilage erosion and synovitis by inhibition of the MMPs and reduction of inflammatory cytokines in SCID-HuRAg mice, which suggests that CD147/HAb18 mAb is a promising treatment option for RA patients.