Landscape of SNPs-mediated lncRNA structural variations and their implication in human complex diseases.

An increasing number of functional studies shows that long noncoding RNAs (lncRNAs) are involved in many aspects of cellular physiology and fulfills a wide variety of regulatory roles at almost every stage of gene expression. A major feature of lncRNAs is the highly folded modular domains in transcripts. With improved modeling and definition, it is now feasible to explore and gain novel insights into the structural-functional relationship of lncRNAs and their association with complex human diseases. In this study, we utilized an automatic computational pipeline to scan lncRNA architecture at the genome-wide scale and to obtain a landscape of functional domains. An accurate alignment algorithm was performed to identify 40 triple pairs between single-nucleotide polymorphisms (SNPs), lncRNAs and diseases. In order to detect the potential contribution of a lncRNA's modular character, we estimated and evaluated structural rearrangements, which were derived from disease-associated SNPs. In addition, we focused on annotating and comparing the global and local heterogeneity of the wild-type and mutant lncRNAs. Assessing lncRNA architecture has yielded how variations in structured regions impact the molecular mechanisms of lncRNAs and how SNPs disturb binding and recruiting ability. These observations are the first glimpse of the 'lncRNA structurome' and make it possible to robustly explore and assemble intricate space conformation and their stress variation. This result also successfully demonstrates that lncRNA transcripts contain a complex structural landscape and highlights the proposed contribution of lncRNA structure in controlling RNA functions and disease mechanisms.

Evaluation of drug efficacy based on the spatial position comparison of drug-target interaction centers.

The spatial position and interaction of drugs and their targets is the most important characteristics for understanding a drug's pharmacological effect, and it could help both in finding new and more precise treatment targets for diseases and in exploring the targeting effects of the new drugs. In this work, we develop a computational pipeline to confirm the spatial interaction relationship of the drugs and their targets and compare the drugs' efficacies based on the interaction centers. First, we produce a 100-sample set to reconstruct a stable docking model of the confirmed drug-target pairs. Second, we set 5.5 Å as the maximum distance threshold for the drug-amino acid residue atom interaction and construct 3-dimensional interaction surface models. Third, by calculating the spatial position of the 3-dimensional interaction surface center, we develop a comparison strategy for estimating the efficacy of different drug-target pairs. For the 1199 drug-target interactions of the 649 drugs and 355 targets, the drugs that have similar interaction center positions tend to have similar efficacies in disease treatment, especially in the analysis of the 37 targeted relationships between the 15 known anti-cancer drugs and 10 target molecules. Furthermore, the analysis of the unpaired anti-cancer drug and target molecules suggests that there is a potential application for discovering new drug actions using the sampling molecular docking and analyzing method. The comparison of the drug-target interaction center spatial position method better reflect the drug-target interaction situations and could support the discovery of new efficacies among the known anti-cancer drugs.

Location deviations of DNA functional elements affected SNP mapping in the published databases and references.

The recent extensive application of next-generation sequencing has led to the rapid accumulation of multiple types of data for functional DNA elements. With the advent of precision medicine, the fine-mapping of risk loci based on these elements has become of paramount importance. In this study, we obtained the human reference genome (GRCh38) and the main DNA sequence elements, including protein-coding genes, miRNAs, lncRNAs and single nucleotide polymorphism flanking sequences, from different repositories. We then realigned these elements to identify their exact locations on the genome. Overall, 5%-20% of all sequence element locations deviated among databases, on the scale of kilobase-pair to megabase-pair. These deviations even affected the selection of genome-wide association study risk-associated genes. Our results implied that the location information for functional DNA elements may deviate among public databases. Researchers should take care when using cross-database sources and should perform pilot sequence alignments before element location-based studies.

Insight into HOTAIR structural features and functions as landing pads for transcription regulation proteins.

LncRNAs fulfill a wide range of regulatory functions at almost every process of gene expression. While derived from secondary structural features, lncRNAs may function as landing pads for transcription factors (TFs). In this paper, we detected the global structural landscape of 20,338 lncRNAs by utilizing a free energy minimization (MFE) algorithm, and identified the interactions between lncRNAs and TFs to analyze molecular association induced by the lncRNA structure. The accessibility analysis of full sequences as well as potential TF-binding fragments shows a large percentage of structural flanking sequence around the TF binding sites. This investigations paid great attention to the high-order architecture of HOTAIR lncRNA, and identified two coincident modular domains covering fragments 171-410bp and 811-1520bp via RNA-TF association predicting and in-silico computation mining. Then, the structural domains were implied potential landing pads to recruit regulatory proteins (13 TFs) and mediated coordinate regulation of transcription. Pathways and diseases enrichment analysis illustrated that the interacted TFs are significantly Pan-cancer relevant which is consistent with the known function of HOTAIR. Overall, the in-depth understanding of HOTAIR structure provides the first glimpse of coordinate regulation driven by modular features. The detailed architectural context could yield broad biological insights and provides a framework for comprehending lncRNA structure-function interrelationships.

LncRNA Structural Characteristics in Epigenetic Regulation.

The rapid development of new generation sequencing technology has deepened the understanding of genomes and functional products. RNA-sequencing studies in mammals show that approximately 85% of the DNA sequences have RNA products, for which the length greater than 200 nucleotides (nt) is called long non-coding RNAs (lncRNA). LncRNAs now have been shown to play important epigenetic regulatory roles in key molecular processes, such as gene expression, genetic imprinting, histone modification, chromatin dynamics, and other activities by forming specific structures and interacting with all kinds of molecules. This paper mainly discusses the correlation between the structure and function of lncRNAs with the recent progress in epigenetic regulation, which is important to the understanding of the mechanism of lncRNAs in physiological and pathological processes.

RNAsmc: A integrated tool for comparing RNA secondary structure and evaluating allosteric effects.

RNA structure plays a crucial role in gene regulation, in RNA stability and the essential biological processes. RNA secondary structure (RSS) motifs are the basic building blocks for investigating the biological mechanisms of structure. Here, we present a strategy for structural motif-based dynamic alignment, namely, RNA secondary-structural motif-comparing (RNAsmc), to identify structural motifs and quantitatively evaluate their underlying molecular functions. RNAsmc also has strong robustness to sequence length, folding protocol and RNA structural profile by chemical probing. Notably, it is also applicable to quantify structural variation in special RNA editing events (SNVs or SNPs, fragment insertion or deletion, etc.). The findings indicate that RNAsmc can uncover the heterogeneity of RNA secondary structure and score for similarities among components, which provides an impetus to cluster RNA families and evaluate allosteric effects. We find that RNAsmc exhibits remarkable detection efficiency for experimentally-derived RiboSNitches. Finally, the pipeline was assembled into an R software package to serve as an automated toolkit to explore, align, and cluster RSS. It is freely available for download at https://CRAN.R-project.org/package=RNAsmc.

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO.

Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules.

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL-SVM.

Feature selection and lung nodule recognition are the core modules of the lung computer-aided detection (Lung CAD) system. To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. First, in view of the poor interpretability of deep features and the incomplete expression of clinically defined handcrafted features, a feature cascade method is proposed to obtain richer feature information of nodules as the final input of the classifier. Second, to better map the global characteristics of samples, the multiple kernel learning support vector machine (MKL-SVM) algorithm with a linear convex combination of polynomial kernel and sigmoid kernel is proposed. Furthermore, this paper applied the methods for speed contraction factor and roulette strategy, and a mixture of simulated annealing (SA) and particle swarm optimization (PSO) is used for global optimization, so as to solve the problem that the PSO is easy to lose particle diversity and fall into the local optimal solution as well as improve the model's training speed. Therefore, the MKL-SVM algorithm is presented in this paper, which is based on swarm intelligence optimization is proposed for lung nodule recognition. Finally, the algorithm construction experiments are conducted on the cooperative hospital dataset and compared with 8 advanced algorithms on the public dataset LUNA16. The experimental results show that the proposed algorithms can improve the accuracy of lung nodule recognition and reduce the missed detection of nodules.

Association of NLRP1 and NLRP3 Polymorphisms with Psoriasis Vulgaris Risk in the Chinese Han Population.

AIM: To clarify the association between the single nucleotide polymorphisms (SNPs) in the NLRP1 and NLRP3 and Psoriasis Vulgaris (PsV) in the Chinese Han population. METHODS: We genotyped eight SNPs, four from NLRP1 (rs8079034, rs11651270, rs11657747, and rs878329) and NLRP3 (rs7512998, rs3806265, rs10754557, and rs10733113) each in 540 patients with PsV and 612 healthy controls in the Chinese Han population using an improved multiplexed ligation detection reaction (iMLDR) method. The genotype and haplotype frequencies were analyzed using a case-control study design. RESULTS: We identified two SNPs, rs3806265 and rs10754557, in NLRP3 that were significantly associated with PsV. The genotype distribution of the rs3806265 SNP was significantly different between cases and controls (p = 0.0451; OR = 0.791; 95% CI = 0.627-0.998). In the recessive model, the genotype distribution of the rs10754557 SNP was significantly different between cases and controls (p = 0.0344; OR = 1.277; 95% CI = 0.987-1.652). The haplotype analysis of rs3806265 and rs10754557 also presented a significant association of TA haplotype with PsV (χ2 = 4.529; p = 0.033). CONCLUSION: NLRP3 may play a role in PsV susceptibility in the Chinese Han population.

Novel CD137 Gene Polymorphisms and Susceptibility to Ischemic Stroke in the Northern Chinese Han Population.

Ischemic stroke is a leading cause of mortality and morbidity worldwide, and atherosclerosis is one of the major risk factors for this neurologic deficit. Recent studies have revealed the important role of CD137 in human atherosclerosis. Here, we analyzed the association of CD137 single nucleotide polymorphisms (SNPs) with ischemic stroke. We assessed three SNPs (rs161827, rs161818, and rs161810) of the CD137 gene and their association with ischemic stroke in a northern Chinese Han population. A total of 496 ischemic stroke patients and 486 gender-matched control subjects were genotyped. We classified these patients according to complications with diabetes and hypertension and also by ischemic stroke subtypes. Allele, genotype, and haplotype association studies were tested in all patients and subgroups. We used multivariable logistic regression analysis combined with 10,000 permutations to analyze the association of CD137 polymorphisms with ischemic stroke. After adjusting for relevant factors, rs161827 was significantly different between patients with and without diabetes and the control group (p = 0.0001, p = 0.014, and p = 0.0001, respectively). In addition, rs161818 and rs161810 differed significantly between patients without diabetes and the control subjects (p = 0.0001 and p = 0.004, respectively). rs161827, rs161818, and rs161810 were all statistically significant among the combination stroke subgroup compared with the controls. These results indicate that the CD137 gene is associated with risk of ischemic stroke in the northern Han Chinese. Moreover, CD137 gene polymorphism may be one mediating factor between diabetes and ischemic stroke.

A Novel Association of the Suppressor of Cytokine Signaling 1 (SOCS1) Gene Polymorphisms in Ischemic Stroke Patients.

Ischemic stroke is a common neurological disease and a leading cause of permanent disability in many countries. Recent studies provide evidence on the role of the suppressor of the cytokine signaling 1 (SOCS1) gene in the development and progression of atherosclerotic lesions. However, few studies have assessed the association between single nucleotide polymorphisms (SNPs) on SOCS1 gene and ischemic stroke. Therefore, the present study aimed to investigate the role of SOCS1 polymorphism in ischemic stroke risk in a northern Chinese Han population. We examined 475 patients with ischemic stroke and 486 normal controls. Three SNPs (rs243327, rs243330, and rs33932899) of SOCS1 gene were determined for TaqMan genotyping assays. We also classified these case samples in depth by complications with hypertension or diabetes and by ischemic stroke subtypes. When adjusting models by multiple factor analysis by logistic regression, then calculated 10,000 permutations were performed for each model to correct the multiple test. Under additive model, the rs243327 was associated with ischemic stroke with hypertension (p = 0.047). Under heterozygous model, the rs33932899 and rs243330 were significantly associated with ischemic stroke subtypes by atherosclerosis (p = 0.038, p = 0.048, respectively). In summary, our data demonstrated for the first time that the polymorphisms of the SOCS1 gene are associated with the risk of ischemic stroke in a northern Chinese Han population, suggesting that SOCS1 gene polymorphisms may play an important role in the pathogenesis of ischemic stroke.