ALBERT

Description: Preferential dysfunction/degeneration of midbrain substantia nigra pars compacta (SNpc) dopaminergic (DA) neurons contributes to the main movement symptoms manifested in Parkinson's disease (PD). Although the Leucine-rich repeat kinase 2 ( LRRK2 ) G2019S missense mutation ( LRRK2 G2019S) is the most common causative genetic factor linked to PD, the effects of LRRK2 G2019S on the function and survival of SNpc DA neurons are poorly understood. Using a binary gene expression system, we generated transgenic mice expressing either wild-type human LRRK2 (WT mice) or the LRRK2 G2019S mutation (G2019S mice) selectively in the midbrain DA neurons. Here we show that overexpression of LRRK2 G2019S did not induce overt motor abnormalities or substantial SNpc DA neuron loss. However, the LRRK2 G2019S mutation impaired dopamine homeostasis and release in aged mice. This reduction in dopamine content/release coincided with the degeneration of DA axon terminals and decreased expression of DA neuron-enriched genes tyrosine hydroxylase (TH), vesicular monoamine transporter 2, dopamine transporter and aldehyde dehydrogenase 1. These factors are responsible for dopamine synthesis, transport and degradation, and their expression is regulated by transcription factor paired-like homeodomain 3 (PITX3). Levels of Pitx3 mRNA and protein were similarly decreased in the SNpc DA neurons of aged G2019S mice. Together, these findings suggest that PITX3-dependent transcription regulation could be one of the many potential mechanisms by which LRRK2 G2019S acts in SNpc DA neurons, resulting in downregulation of its downstream target genes critical for dopamine homeostasis and release.

Description: Motivation: Identifying microRNAs associated with diseases (disease miRNAs) is helpful for exploring the pathogenesis of diseases. Because miRNAs fulfill function via the regulation of their target genes and because the current number of experimentally validated targets is insufficient, some existing methods have inferred potential disease miRNAs based on the predicted targets. It is difficult for these methods to achieve excellent performance due to the high false-positive and false-negative rates for the target prediction results. Alternatively, several methods have constructed a network composed of miRNAs based on their associated diseases and have exploited the information within the network to predict the disease miRNAs. However, these methods have failed to take into account the prior information regarding the network nodes and the respective local topological structures of the different categories of nodes. Therefore, it is essential to develop a method that exploits the more useful information to predict reliable disease miRNA candidates. Results: miRNAs with similar functions are normally associated with similar diseases and vice versa. Therefore, the functional similarity between a pair of miRNAs is calculated based on their associated diseases to construct a miRNA network. We present a new prediction method based on random walk on the network. For the diseases with some known related miRNAs, the network nodes are divided into labeled nodes and unlabeled nodes, and the transition matrices are established for the two categories of nodes. Furthermore, different categories of nodes have different transition weights. In this way, the prior information of nodes can be completely exploited. Simultaneously, the various ranges of topologies around the different categories of nodes are integrated. In addition, how far the walker can go away from the labeled nodes is controlled by restarting the walking. This is helpful for relieving the negative effect of noisy data. For the diseases without any known related miRNAs, we extend the walking on a miRNA-disease bilayer network. During the prediction process, the similarity between diseases, the similarity between miRNAs, the known miRNA-disease associations and the topology information of the bilayer network are exploited. Moreover, the importance of information from different layers of network is considered. Our method achieves superior performance for 18 human diseases with AUC values ranging from 0.786 to 0.945. Moreover, case studies on breast neoplasms, lung neoplasms, prostatic neoplasms and 32 diseases further confirm the ability of our method to discover potential disease miRNAs. Availability and implementation: A web service for the prediction and analysis of disease miRNAs is available at http://bioinfolab.stx.hk/midp/ . Contact: guoyahong_hlju@163.com or lixia@hrbmu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Position weight matrices (PWMs) are the standard model for DNA and RNA regulatory motifs. In PWMs nucleotide probabilities are independent of nucleotides at other positions. Models that account for dependencies need many parameters and are prone to overfitting. We have developed a Bayesian approach for motif discovery using Markov models in which conditional probabilities of order k – 1 act as priors for those of order k . This Bayesian Markov model (BaMM) training automatically adapts model complexity to the amount of available data. We also derive an EM algorithm for de-novo discovery of enriched motifs. For transcription factor binding, BaMMs achieve significantly ( P = 1/16) higher cross-validated partial AUC than PWMs in 97% of 446 ChIP-seq ENCODE datasets and improve performance by 36% on average. BaMMs also learn complex multipartite motifs, improving predictions of transcription start sites, polyadenylation sites, bacterial pause sites, and RNA binding sites by 26–101%. BaMMs never performed worse than PWMs. These robust improvements argue in favour of generally replacing PWMs by BaMMs.

Description: A new CO 2 cryogenic capture and liquefaction system has been proposed previously in order to separate CO 2 from exhausted gases and make it as a resource for industry. This system combines CO 2 cryogenic capture with N 2 /O 2 separation together. Its energy consumption is lower than the traditional amine solution capture process as theoretical analysis. In this study, the simulation of the proposed system with several improvements was carried out aiming to reduce the energy consumption further. Many heat exchangers were introduced and the heat exchanger arrangements were optimized to recycle the refrigeration capacity from the returned N 2 after the N 2 /O 2 separation. The discharge pressure of mixture gas from the compressor was reduced from 10 to 3.493 MPa. The simulation results showed that the compression work could be greatly reduced and the energy consumption of CO 2 capture in this new system after these improvements reached 2.884 GJ/ton CO 2 . The new system is promising because not only liquid or solid CO 2 could be produced but also N 2 and O 2 could be separated.

Description: The IL12B gene encodes the common p40 subunit of IL-12 and IL-23, cytokines with key roles in Th1 and Th17 biology, respectively, and genetic variation in this region significantly influences risk of psoriasis. Here, we demonstrate that a psoriasis-associated risk haplotype at the IL12B locus leads to increased expression of IL12B by monocytes and correlated with increased serum levels of IL-12, IFN- and the IFN- induced chemokine, CXCL10. In contrast, serum IL-23 levels were decreased in risk carriers when compared with non-carriers. We further demonstrate that IL-12 is increased in psoriatic skin and that risk carriers manifest a skewing of the inflammatory network toward stronger IFN- responses. Taken together, our data demonstrate that the risk variant in IL12B associates with its increased expression and predisposes to stronger Th1 polarization through deviation of the local inflammatory environment toward increased IL-12/IFN- at the expense of IL-23/IL-17 responses.

Description: Most previously reported RNA-cleaving DNAzymes require only a single divalent metal ion for catalysis. We recently reported a general trivalent lanthanide-dependent DNAzyme named Ce13d. This work shows that Ce13d requires both Na + and a trivalent lanthanide (e.g. Ce 3+ ), simultaneously. This discovery is facilitated by the sequence similarity between Ce13d and a recently reported Na + -specific DNAzyme, NaA43. The Ce13d cleavage rate linearly depends on the concentration of both metal ions. Sensitized Tb 3+ luminescence and DMS footprinting experiments indicate that the guanines in the enzyme loop are important for Na + -binding. The Na + dissociation constants of Ce13d measured from the cleavage activity assay, Tb 3+ luminescence and DMS footprinting are 24.6, 16.3 and 47 mM, respectively. Mutation studies indicate that the role of Ce 3+ might be replaced by G 23 in NaA43. Ce 3+ functions by stabilizing the transition state phosphorane, thus promoting cleavage. G 23 competes favorably with low concentration Ce 3+ (below 1 μM). The G 23 -to-hypoxanthine mutation suggests the N1 position of the guanine as a hydrogen bond donor. Together, Ce13d has two distinct metal binding sites, each fulfilling a different role. DNAzymes can be quite sophisticated in utilizing metal ions for catalysis and molecular recognition, similar to protein metalloenzymes.

Description: The MPI Bioinformatics Toolkit ( http://toolkit.tuebingen.mpg.de ) is an open, interactive web service for comprehensive and collaborative protein bioinformatic analysis. It offers a wide array of interconnected, state-of-the-art bioinformatics tools to experts and non-experts alike, developed both externally (e.g. BLAST+, HMMER3, MUSCLE) and internally (e.g. HHpred, HHblits, PCOILS). While a beta version of the Toolkit was released 10 years ago, the current production-level release has been available since 2008 and has serviced more than 1.6 million external user queries. The usage of the Toolkit has continued to increase linearly over the years, reaching more than 400 000 queries in 2015. In fact, through the breadth of its tools and their tight interconnection, the Toolkit has become an excellent platform for experimental scientists as well as a useful resource for teaching bioinformatic inquiry to students in the life sciences. In this article, we report on the evolution of the Toolkit over the last ten years, focusing on the expansion of the tool repertoire (e.g. CS-BLAST, HHblits) and on infrastructural work needed to remain operative in a changing web environment.

Description: Motivation: The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods. Results: Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2% of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision. Availability and Implementation: The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/ . Contacts: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: MicroRNAs (miRNAs) play critical roles in gene regulation. Although it is well known that multiple miRNAs may work as miRNA modules to synergistically regulate common target mRNAs, the understanding of miRNA modules is still in its infancy. Results: We employed the recently generated high throughput experimental data to study miRNA modules. We predicted 181 miRNA modules and 306 potential miRNA modules. We observed that the target sites of these predicted modules were in general weaker compared with those not bound by miRNA modules. We also discovered that miRNAs in predicted modules preferred to bind unconventional target sites rather than canonical sites. Surprisingly, contrary to a previous study, we found that most adjacent miRNA target sites from the same miRNA modules were not within the range of 10–130 nucleotides. Interestingly, the distance of target sites bound by miRNAs in the same modules was shorter when miRNA modules bound unconventional instead of canonical sites. Our study shed new light on miRNA binding and miRNA target sites, which will likely advance our understanding of miRNA regulation. Availability and implementation: The software miRModule can be freely downloaded at http://hulab.ucf.edu/research/projects/miRNA/miRModule . Supplementary information: Supplementary data are available at Bioinformatics online. Contact: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu .

Description: Resistin is a polypeptide hormone that was reported to be associated with insulin resistance, inflammation and risk of type 2 diabetes and cardiovascular disease. We conducted a genome-wide association (GWA) study on circulating resistin levels in individuals of European ancestry drawn from the two independent studies: the Nurses' Health Study ( n = 1590) and the Health, Aging and Body Composition Study ( n = 1658). Single-nucleotide polymorphisms (SNPs) identified in the GWA analysis were replicated in an independent cohort of Europeans: the Gargano Family Study ( n = 659). We confirmed the association with a previously known locus, the RETN gene (19p13.2), and identified two novel loci near the TYW3/CRYZ gene (1p31) and the NDST4 gene (4q25), associated with resistin levels at a genome-wide significant level, best represented by SNP rs3931020 ( P = 6.37 x 10 –12 ) and SNP rs13144478 ( P = 6.19 x 10 –18 ), respectively. Gene expression quantitative trait loci analyses showed a significant cis association between the SNP rs3931020 and CRYZ gene expression levels ( P = 3.68 x 10 –7 ). We also found that both of these two SNPs were significantly associated with resistin gene ( RETN ) mRNA levels in white blood cells from 68 subjects with type 2 diabetes (both P = 0.02). In addition, the resistin-rising allele of the TYW3/CRYZ SNP rs3931020, but not the NDST4 SNP rs13144478, showed a consistent association with increased coronary heart disease risk [odds ratio = 1.18 (95% CI, 1.03–1.34); P = 0.01]. Our results suggest that genetic variants in TYW3/CRYZ and NDST4 loci may be involved in the regulation of circulating resistin levels. More studies are needed to verify the associations of the SNP rs13144478 with NDST4 gene expression and resistin-related disease.