Implantable Epigallocatechin Gallate Sustained-Release Nanofibers for the Prevention of Immobilization-Induced Muscle Atrophy.

Long-term immobilization of joints can lead to disuse atrophy of the muscles in the joints. Oral nutrients are used clinically for rehabilitation and therapeutic purposes, but bioavailability and targeting are limited. Here, we report tea polyphenols (dietary polyphenols), sustained-release nanofilms that release tea polyphenols through slow local degradation of core-shell nanofibers in muscles. This dietary polyphenol does not require gastrointestinal consumption and multiple doses and can directly remove inflammatory factors and superoxide generated in muscle tissue during joint fixation. The quality of muscles is increased by 30%, and muscle movement function is effectively improved. Although nanofibers need to be implanted into muscles, they can improve bacterial infections after joint surgery. To investigate the biological mechanism of this core-shell nanomembrane prevention, we conducted further transcriptomic studies on muscle, confirming that in addition to achieving antioxidation and anti-inflammation by inhibiting TNF-α and NF-κB signaling pathways, tea polyphenol core-shell nanofibers can also promote muscle formation by activating the p-Akt signaling pathway.

Accurate in silico confirmation of rare copy number variant calls from exome sequencing data using transfer learning.

Exome sequencing is widely used in genetic studies of human diseases and clinical genetic diagnosis. Accurate detection of copy number variants (CNVs) is important to fully utilize exome sequencing data. However, exome data are noisy. None of the existing methods alone can achieve both high precision and recall rate. A common practice is to perform heuristic filtration followed by manual inspection of read depth of putative CNVs. This approach does not scale in large studies. To address this issue, we developed a transfer learning method, CNV-espresso, for in silico confirming rare CNVs from exome sequencing data. CNV-espresso encodes candidate CNVs from exome data as images and uses pretrained convolutional neural network models to classify copy number states. We trained CNV-espresso using an offspring-parents trio exome sequencing dataset, with inherited CNVs as positives and CNVs with Mendelian errors as negatives. We evaluated the performance using additional samples that have both exome and whole-genome sequencing (WGS) data. Assuming the CNVs detected from WGS data as a proxy of ground truth, CNV-espresso significantly improves precision while keeping recall almost intact, especially for CNVs that span a small number of exons. CNV-espresso can effectively replace manual inspection of CNVs in large-scale exome sequencing studies.

HIF-1α promotes the proliferation and migration of pulmonary arterial smooth muscle cells via activation of Cx43.

The proliferation of pulmonary artery smooth muscle cells (PASMCs) is an important cause of pulmonary vascular remodelling in hypoxia-induced pulmonary hypertension (HPH). However, its underlying mechanism has not been well elucidated. Connexin 43 (Cx43) plays crucial roles in vascular smooth muscle cell proliferation in various cardiovascular diseases. Here, the male Sprague-Dawley (SD) rats were exposed to hypoxia (10% O2 ) for 21 days to induce rat HPH model. PASMCs were treated with CoCl2 (200 µM) for 24 h to establish the HPH cell model. It was found that hypoxia up-regulated the expression of Cx43 and phosphorylation of Cx43 at Ser 368 in rat pulmonary arteries and PASMCs, and stimulated the proliferation and migration of PASMCs. HIF-1α inhibitor echinomycin attenuated the CoCl2 -induced Cx43 expression and phosphorylation of Cx43 at Ser 368 in PASMCs. The interaction between HIF-1α and Cx43 promotor was also identified using chromatin immunoprecipitation assay. Moreover, Cx43 specific blocker (37,43 Gap27) or knockdown of Cx43 efficiently alleviated the proliferation and migration of PASMCs under chemically induced hypoxia. Therefore, the results above suggest that HIF-1α, as an upstream regulator, promotes the expression of Cx43, and the HIF-1α/Cx43 axis regulates the proliferation and migration of PASMCs in HPH.

Exosomal connexin 43 regulates the resistance of glioma cells to temozolomide.

Glioblastoma is the most common and aggressive brain tumor and it is characterized by a high mortality rate. Temozolomide (TMZ) is an effective chemotherapy drug for glioblastoma, but the resistance to TMZ has come to represent a major clinical problem, and its underlying mechanism has yet to be elucidated. In the present study, the role of exosomal connexin 43 (Cx43) in the resistance of glioma cells to TMZ and cell migration was investigated. First, higher expression levels of Cx43 were detected in TMZ­resistant U251 (U251r) cells compared with those in TMZ­sensitive (U251s) cells. Exosomes from U251s or U251r cells (sExo and rExo, respectively) were isolated. It was found that the expression of Cx43 in rExo was notably higher compared with that in sExo, whereas treatment with rExo increased the expression of Cx43 in U251s cells. Additionally, exosomes stained with dioctadecyloxacarbocyanine (Dio) were used to visualized exosome uptake by glioma cells. It was observed that the uptake of Dio­stained rExo in U251s cells was more prominent compared with that of Dio­stained sExo, while 37,43Gap27, a gap junction mimetic peptide directed against Cx43, alleviated the rExo uptake by cells. Moreover, rExo increased the IC50 of U251s to TMZ, colony formation and Bcl­2 expression, but decreased Bax and cleaved caspase­3 expression in U251s cells. 37,43Gap27 efficiently inhibited these effects of rExo on U251s cells. Finally, the results of the wound healing and Transwell assays revealed that rExo significantly enhanced the migration of U251s cells, whereas 37,43Gap27 significantly attenuated rExo­induced cell migration. Taken together, these results indicate the crucial role of exosomal Cx43 in chemotherapy resistance and migration of glioma cells, and suggest that Cx43 may hold promise as a therapeutic target for glioblastoma in the future.

ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function.

BACKGROUND: During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Recent studies show that optimizing the parameters for hidden Markov model, as well as integrating hidden Markov model with partition function can raise the accuracy of alignment. The combination of partition function and optimized HMM, which could further improve the alignment's accuracy, however, was ignored by these researches. RESULTS: A novel algorithm for MSA called ProbPFP is presented in this paper. It intergrate optimized HMM by particle swarm with partition function. The algorithm of PSO was applied to optimize HMM's parameters. After that, the posterior probability obtained by the HMM was combined with the one obtained by partition function, and thus to calculate an integrated substitution score for alignment. In order to evaluate the effectiveness of ProbPFP, we compared it with 13 outstanding or classic MSA methods. The results demonstrate that the alignments obtained by ProbPFP got the maximum mean TC scores and mean SP scores on these two benchmark datasets: SABmark and OXBench, and it got the second highest mean TC scores and mean SP scores on the benchmark dataset BAliBASE. ProbPFP is also compared with 4 other outstanding methods, by reconstructing the phylogenetic trees for six protein families extracted from the database TreeFam, based on the alignments obtained by these 5 methods. The result indicates that the reference trees are closer to the phylogenetic trees reconstructed from the alignments obtained by ProbPFP than the other methods. CONCLUSIONS: We propose a new multiple sequence alignment method combining optimized HMM and partition function in this paper. The performance validates this method could make a great improvement of the alignment's accuracy.

Integrated entropy-based approach for analyzing exons and introns in DNA sequences.

BACKGROUND: Numerous essential algorithms and methods, including entropy-based quantitative methods, have been developed to analyze complex DNA sequences since the last decade. Exons and introns are the most notable components of DNA and their identification and prediction are always the focus of state-of-the-art research. RESULTS: In this study, we designed an integrated entropy-based analysis approach, which involves modified topological entropy calculation, genomic signal processing (GSP) method and singular value decomposition (SVD), to investigate exons and introns in DNA sequences. We optimized and implemented the topological entropy and the generalized topological entropy to calculate the complexity of DNA sequences, highlighting the characteristics of repetition sequences. By comparing digitalizing entropy values of exons and introns, we observed that they are significantly different. After we converted DNA data to numerical topological entropy value, we applied SVD method to effectively investigate exon and intron regions on a single gene sequence. Additionally, several genes across five species are used for exon predictions. CONCLUSIONS: Our approach not only helps to explore the complexity of DNA sequence and its functional elements, but also provides an entropy-based GSP method to analyze exon and intron regions. Our work is feasible across different species and extendable to analyze other components in both coding and noncoding region of DNA sequences.

Exposing the Causal Effect of Body Mass Index on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study.

Introduction: High body mass index (BMI) is a positive associated phenotype of type 2 diabetes mellitus (T2DM). Abundant studies have observed this from a clinical perspective. Since the rapid increase in a large number of genetic variants from the genome-wide association studies (GWAS), common SNPs of BMI and T2DM were identified as the genetic basis for understanding their associations. Currently, their causality is beginning to blur. Materials and Methods: To classify it, a Mendelian randomisation (MR), using genetic instrumental variables (IVs) to explore the causality of intermediate phenotype and disease, was utilized here to test the effect of BMI on the risk of T2DM. In this article, MR was carried out on GWAS data using 52 independent BMI SNPs as IVs. The pooled odds ratio (OR) of these SNPs was calculated using inverse-variance weighted method for the assessment of 5 kg/m2 higher BMI on the risk of T2DM. The leave-one-out validation was conducted to identify the effect of individual SNPs. MR-Egger regression was utilized to detect potential pleiotropic bias of variants. Results: We obtained the high OR (1.470; 95% CI 1.170 to 1.847; P = 0.001), low intercept (0.004, P = 0.661), and small fluctuation of ORs {from -0.039 [(1.412 - 1.470) / 1.470)] to 0.075 [(1.568- 1.470) / 1.470)] in leave-one-out validation. Conclusion: We validate the causal effect of high BMI on the risk of T2DM. The low intercept shows no pleiotropic bias of IVs. The small alterations of ORs activated by removing individual SNPs showed no single SNP drives our estimate.

ERDS-exome: a Hybrid Approach for Copy Number Variant Detection from Whole-exome Sequencing Data.

Copy number variants (CNVs) play important roles in human disease and evolution. With the rapid development of next-generation sequencing technologies, many tools have been developed for inferring CNVs based on whole-exome sequencing (WES) data. However, as a result of the sparse distribution of exons in the genome, the limitations of the WES technique, and the nature of high-level signal noises in WES data, the efficacy of these variants remains less than desirable. Thus, there is need for the development of an effective tool to achieve a considerable power in WES CNVs discovery. In the present study, we describe a novel method, Estimation by Read Depth (RD) with Single-nucleotide variants from exome sequencing data (ERDS-exome). ERDS-exome employs a hybrid normalization approach to normalize WES data and to incorporate RD and single-nucleotide variation information together as a hybrid signal into a paired hidden Markov model to infer CNVs from WES data. Based on systematic evaluations of real data from the 1000 Genomes Project using other state-of-the-art tools, we observed that ERDS-exome demonstrates higher sensitivity and provides comparable or even better specificity than other tools. ERDS-exome is publicly available at: https://erds-exome.github.io.

Analyzing large-scale samples confirms the association between the rs1051730 polymorphism and lung cancer susceptibility.

The early genome-wide association studies (GWAS) found a significant association between lung cancer and rs1051730 (15q25) polymorphism. However, the subsequent studies reported consistent and inconsistent results in different populations. Three meta-analysis studies were thus performed to reevaluate the association. But their results remain inconsistent. After that, some new GWAS studies reported conflicting results again. We think that the divergence of these results may be due to small-scale samples or heterogeneity among different populations. Therefore, we reevaluated the association by collecting more samples (N = 33,617 cases and 116,639 controls) from 31 studies, which incorporate 8 new studies and 23 previous studies used by one or more of the three meta-analysis studies. We observed a significant association between lung cancer and rs1051730 in pooled population by using allele (OR = 1.30, 95% CI = 1.27-1.34, P < 0.0001), dominant (OR = 1.41, 95% CI = 1.29-1.55, P < 0.0001), recessive (OR = 1.53, 95% CI = 1.42-1.65, P < 0.0001) and additive (OR = 1.75, 95% CI = 1.61-1.90, P < 0.0001) models. Through the subgroup analysis, we observed a significant heterogeneity only in East Asian population (P = 0.006, I(2) = 66.9%), and the association is significant in all subgroups (OR = 1.2976, 95% CI = 1.2622-1.3339 (European ancestry), OR = 1.5025, 95% CI = 1.2465-1.8110 (African), OR = 1.7818, 95% CI = 1.3915-2.2815 (East Asian), P < 0.0001). We believe that these results will contribute to understanding the genetic mechanism of lung cancer.

LncRNA2Function: a comprehensive resource for functional investigation of human lncRNAs based on RNA-seq data.

BACKGROUND: The GENCODE project has collected over 10,000 human long non-coding RNA (lncRNA) genes. However, the vast majority of them remain to be functionally characterized. Computational investigation of potential functions of human lncRNA genes is helpful to guide further experimental studies on lncRNAs. RESULTS: In this study, based on expression correlation between lncRNAs and protein-coding genes across 19 human normal tissues, we used the hypergeometric test to functionally annotate a single lncRNA or a set of lncRNAs with significantly enriched functional terms among the protein-coding genes that are significantly co-expressed with the lncRNA(s). The functional terms include all nodes in the Gene Ontology (GO) and 4,380 human biological pathways collected from 12 pathway databases. We successfully mapped 9,625 human lncRNA genes to GO terms and biological pathways, and then developed the first ontology-driven user-friendly web interface named lncRNA2Function, which enables researchers to browse the lncRNAs associated with a specific functional term, the functional terms associated with a specific lncRNA, or to assign functional terms to a set of human lncRNA genes, such as a cluster of co-expressed lncRNAs. The lncRNA2Function is freely available at http://mlg.hit.edu.cn/lncrna2function. CONCLUSIONS: The LncRNA2Function is an important resource for further investigating the functions of a single human lncRNA, or functionally annotating a set of human lncRNAs of interest.

LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression.

Long non-coding RNAs (lncRNAs) have emerged as critical regulators of genes at epigenetic, transcriptional and post-transcriptional levels, yet what genes are regulated by a specific lncRNA remains to be characterized. To assess the effects of the lncRNA on gene expression, an increasing number of researchers profiled the genome-wide or individual gene expression level change after knocking down or overexpressing the lncRNA. Herein, we describe a curated database named LncRNA2Target, which stores lncRNA-to-target genes and is publicly accessible at http://www.lncrna2target.org. A gene was considered as a target of a lncRNA if it is differentially expressed after the lncRNA knockdown or overexpression. LncRNA2Target provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene. Both search types are performed either by browsing a provided catalog of lncRNA names or by inserting lncRNA/target gene IDs/names in a search box.

A gradient-boosting approach for filtering de novo mutations in parent-offspring trios.

MOTIVATION: Whole-genome and -exome sequencing on parent-offspring trios is a powerful approach to identifying disease-associated genes by detecting de novo mutations in patients. Accurate detection of de novo mutations from sequencing data is a critical step in trio-based genetic studies. Existing bioinformatic approaches usually yield high error rates due to sequencing artifacts and alignment issues, which may either miss true de novo mutations or call too many false ones, making downstream validation and analysis difficult. In particular, current approaches have much worse specificity than sensitivity, and developing effective filters to discriminate genuine from spurious de novo mutations remains an unsolved challenge. RESULTS: In this article, we curated 59 sequence features in whole genome and exome alignment context which are considered to be relevant to discriminating true de novo mutations from artifacts, and then employed a machine-learning approach to classify candidates as true or false de novo mutations. Specifically, we built a classifier, named De Novo Mutation Filter (DNMFilter), using gradient boosting as the classification algorithm. We built the training set using experimentally validated true and false de novo mutations as well as collected false de novo mutations from an in-house large-scale exome-sequencing project. We evaluated DNMFilter's theoretical performance and investigated relative importance of different sequence features on the classification accuracy. Finally, we applied DNMFilter on our in-house whole exome trios and one CEU trio from the 1000 Genomes Project and found that DNMFilter could be coupled with commonly used de novo mutation detection approaches as an effective filtering approach to significantly reduce false discovery rate without sacrificing sensitivity. AVAILABILITY: The software DNMFilter implemented using a combination of Java and R is freely available from the website at http://humangenome.duke.edu/software.

An evaluation of copy number variation detection tools from whole-exome sequencing data.

Copy number variation (CNV) has been found to play an important role in human disease. Next-generation sequencing technology, including whole-genome sequencing (WGS) and whole-exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well-known WES-based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome-based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well-known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.

A generalized topological entropy for analyzing the complexity of DNA sequences.

Topological entropy is one of the most difficult entropies to be used to analyze the DNA sequences, due to the finite sample and high-dimensionality problems. In order to overcome these problems, a generalized topological entropy is introduced. The relationship between the topological entropy and the generalized topological entropy is compared, which shows the topological entropy is a special case of the generalized entropy. As an application the generalized topological entropy in introns, exons and promoter regions was computed, respectively. The results indicate that the entropy of introns is higher than that of exons, and the entropy of the exons is higher than that of the promoter regions for each chromosome, which suggest that DNA sequence of the promoter regions is more regular than the exons and introns.

Disease-driven detection of differential inherited SNP modules from SNP network.

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.