Integrating Overlapping Structures and Background Information of Words Significantly Improves Biological Sequence Comparison

Word-based models have achieved promising results in sequence comparison. However, as the important statistical properties of words in biological sequence, how to use the overlapping structures and background information of the words to improve sequence comparison is still a problem. This paper proposed a new statistical method that integrates the overlapping structures and the background information of the words in biological sequences. To assess the effectiveness of this integration for sequence comparison, two sets of evaluation experiments were taken to test the proposed model. The first one, performed via receiver operating curve analysis, is the application of proposed method in discrimination between functionally related regulatory sequences and unrelated sequences, intron and exon. The second experiment is to evaluate the performance of the proposed method with f-measure for clustering Hepatitis E virus genotypes. It was demonstrated that the proposed method integrating the overlapping structures and the background information of words significantly improves biological sequence comparison and outperforms the existing models

Multimodality Molecular Imaging of Cardiovascular Disease Based on Nanoprobes

Recently, multimodality molecular imaging has evolved into a fast-growing research field with goals of detecting and measuring biological processes in vivo non-invasively. Researchers have come to realize that the complementary abilities of different imaging modalities over single modality could provide more precisely information for the diagnosis of diseases. At present, nanoparticles-based multimodal imaging probes have received significant attention because of their ease of preparation and straightforward integration of each modality into one entity. More importantly, nanotechnology has an increasing impact on multimodality molecular imaging of cardiovascular diseases, such as atherosclerosis and vulnerable plaque, myocardial infarction, angiogenesis, apoptosis and so on. In this review, we briefly summarize that various nanoprobes are exploited for targeted molecular imaging of cardiovascular diseases, as well as associated multimodality imaging approaches and their applications in the diagnosis and treatment of cardiovascular diseases

Turning an antiviral into an anticancer drug: Nanoparticle delivery of acyclovir monophosphate

Anti-herpes simplex virus (HSV) drug acyclovir (ACV) is phosphorylated by the viral thymidine kinase (TK), but not the cellular TK. Phosphorylated ACV inhibits cellular DNA synthesis and kills the infected cells. We hypothesize that ACV monophosphate (ACVP), which is an activated metabolite of ACV, should be efficient in killing cells independent of HSV-TK. If so, ACVP should be a cytotoxic agent if properly delivered to the cancer cells. The Lipid/Calcium/Phosphate (LCP) nanoparticles (NPs) with a membrane/core structure were used to encapsulate ACVP to facilitate the targeted delivery of ACVP to the tumor. The LCP NPs showed entrapment efficiency of ~69%, the nano-scaled particle size and positive zeta potential. Moreover, ACVP-loaded LCP NPs (A-LCP NPs) exhibited concentration-dependent cytotoxicity against H460 cells and increased S-phase arrest. More importantly, a significant reduction of the tumor volume over 4 days following administration (p<0.05~0.005) of A-LCP NPs, suggests excellent in vivo efficacy. Whereas, two free drugs (ACV and ACVP) and blank LCP NPs showed little or no therapeutic effect. It was also found that the high efficacy of A-LCP NPs was associated with the ability to induce dramatic apoptosis of the tumor cells, as well as significantly inhibit tumor cell proliferation and cell cycle progression. In conclusion, with the help of LCP NPs, monophosphorylation modification of ACV can successfully modify an HSV-TK-dependent antiviral drug into an anti-tumor drug

Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks

With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increasingly important. Statistical or traditional machine learning methods are inefficient, and improved accuracy is needed. The methods based on deep learning cannot directly process non-Euclidean spatial data, such as cell diagrams. In this study, we developed scGAEGAT, a multi-modal model with graph autoencoders and graph attention networks for scRNA-seq analysis based on graph neural networks. Cosine similarity, median L1 distance, and root-mean-squared error were used to measure the gene imputation performance of different methods for comparison with scGAEGAT. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score were used to measure the cell clustering performance of different methods for comparison with scGAEGAT. Experimental results demonstrated promising performance of the scGAEGAT model in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels

A capsule network-based method for identifying transcription factors

Transcription factors (TFs) are typical regulators for gene expression and play versatile roles in cellular processes. Since it is time-consuming, costly, and labor-intensive to detect it by using physical methods, it is desired to develop a computational method to detect TFs. Here, we presented a capsule network-based method for identifying TFs. This method is an end-to-end deep learning method, consisting mainly of an embedding layer, bidirectional long short-term memory (LSTM) layer, capsule network layer, and three fully connected layers. The presented method obtained an accuracy of 0.8820, being superior to the state-of-the-art methods. These empirical experiments showed that the inclusion of the capsule network promoted great performances and that the capsule network-based representation was superior to the property-based representation for distinguishing between TFs and non-TFs. We also implemented the presented method into a user-friendly web server, which is freely available at http://www.biolscience.cn/Capsule_TF/ for all scientific researchers

Curcumin Micelles Remodel Tumor Microenvironment and Enhance Vaccine Activity in an Advanced Melanoma Model

Previously, we have reported a lipid-based Trp2 peptide vaccine for immunotherapy against melanoma. The suppressive immune microenvironment in the tumor is a major hurdle for an effective vaccine therapy. We hypothesized that curcumin (CUR) would remodel the tumor microenvironment to improve the vaccine activity. Curcumin–polyethylene glycol conjugate (CUR–PEG), an amphiphilic CUR-based micelle, was delivered intravenously (i.v.) to the tumor. Indeed, in the B16F10 tumor–bearing mice, the combination of CUR–PEG and vaccine treatment resulted in a synergistic antitumor effect (P < 0.001) compared to individual treatments. In the immune organs, the combination therapy significantly boosted in vivo cytotoxic T-lymphocyte response (41.0 ± 5.0% specific killing) and interferon-γ (IFN-γ) production (sevenfold increase). In the tumor microenvironment, the combination therapy led to significantly downregulated levels of immunosuppressive factors, such as decreased numbers of myeloid-derived suppressor cells and regulatory T cells (Treg) cells and declined levels of interleukin-6 and chemokine ligand 2—in correlation with increased levels of proinflammatory cytokines, including tumor necrosis factor-α and IFN-γ as well as an elevation in the CD8+ T-cell population. The results indicated a distinct M2 to M1 phenotype switch in the treated tumors. Combining CUR–PEG and vaccine also dramatically downregulated the signal transducer and activator of transcription 3 pathway (76% reduction). Thus, we conclude that CUR–PEG is an effective agent to improve immunotherapy for advanced melanoma

Pathological Comparisons of the Hippocampal Changes in the Transient and Permanent Middle Cerebral Artery Occlusion Rat Models

© Copyright © 2019 Shah, Li, Kury, Zeb, Khatoon, Liu, Yang, Liu, Yao, Khan, Koh, Jiang and Li. Ischemic strokes are categorized by permanent or transient obstruction of blood flow, which impedes delivery of oxygen and essential nutrients to brain. In the last decade, the therapeutic window for tPA has increased from 3 to 5–6 h, and a new technique, involving the mechanical removal of the clot (endovascular thrombectomy) to allow reperfusion of the injured area, is being used more often. This last therapeutic approach can be done until 24 h after stroke onset. Due to this fact, more acute ischemic stroke patients are now being recanalized, and so tMCAO is probably the “best” model to address these patients that have a potential good outcome in terms of survival and functional recovery. However, permanent occlusion patients are also important, not only to increase survival rate but also to improve functional outcomes, although these are more difficult to achieve. So, both models are important, and which target different stroke patients in the clinical scenario. Hippocampus has a vital role in memory and cognition, is prone to ischemic induced neurodegeneration. This study was designed to delineate the molecular, pathological, and neurological changes in rat models of t-MCAO, permanent MCAO (pMCAO), and pMCAO with diabetic conditions in hippocampal tissue. Our results showed that these three models showed distinct discrepancies at numerous pathological process, including key signaling molecules involved in neuronal apoptosis, glutamate induced excitotoxicity, neuroinflammation, oxidative stress, and neurotrophic changes. Our result suggests that the two commonly used MCAO models exhibited tremendous differences in terms of neuronal cell loss, glutamate excitotoxic related signaling, synaptic transmission markers, neuron inflammatory and oxidative stress molecules. These differences may reflect the variations in different models, which may provide valuable information for mechanistic and therapeutic inconsistences as experienced in both preclinical models and clinical trials

Causal relationship between gut microbiota and risk of gastroesophageal reflux disease: a genetic correlation and bidirectional Mendelian randomization study

BackgroundNumerous observational studies have identified a linkage between the gut microbiota and gastroesophageal reflux disease (GERD). However, a clear causative association between the gut microbiota and GERD has yet to be definitively ascertained, given the presence of confounding variables.MethodsThe genome-wide association study (GWAS) pertaining to the microbiome, conducted by the MiBioGen consortium and comprising 18,340 samples from 24 population-based cohorts, served as the exposure dataset. Summary-level data for GERD were obtained from a recent publicly available genome-wide association involving 78 707 GERD cases and 288 734 controls of European descent. The inverse variance-weighted (IVW) method was performed as a primary analysis, the other four methods were used as supporting analyses. Furthermore, sensitivity analyses encompassing Cochran’s Q statistics, MR-Egger intercept, MR-PRESSO global test, and leave-one-out methodology were carried out to identify potential heterogeneity and horizontal pleiotropy. Ultimately, a reverse MR assessment was conducted to investigate the potential for reverse causation.ResultsThe IVW method’s findings suggested protective roles against GERD for the Family Clostridiales Vadin BB60 group (P = 0.027), Genus Lachnospiraceae UCG004 (P = 0.026), Genus Methanobrevibacter (P = 0.026), and Phylum Actinobacteria (P = 0.019). In contrast, Class Mollicutes (P = 0.037), Genus Anaerostipes (P = 0.049), and Phylum Tenericutes (P = 0.024) emerged as potential GERD risk factors. In assessing reverse causation with GERD as the exposure and gut microbiota as the outcome, the findings indicate that GERD leads to dysbiosis in 13 distinct gut microbiota classes. The MR results’ reliability was confirmed by thorough assessments of heterogeneity and pleiotropy.ConclusionsFor the first time, the MR analysis indicates a genetic link between gut microbiota abundance changes and GERD risk. This not only substantiates the potential of intestinal microecological therapy for GERD, but also establishes a basis for advanced research into the role of intestinal microbiota in the etiology of GERD

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers