Comparative RNA Genomics

lncRNA SNHG12 Inhibition Based on Microsystem Cell Imaging Technology Protects the Endothelium from LPS-Induced Inflammation by Inhibiting the Expression of miR-140-3p Target Gene fndc5

Acute lung injury (ALI) is a serious disease with a high incidence rate, characterized by uncontrolled inflammation and apoptosis. At present, long-chain noncoding RNA (lncRNA) is a noncoding RNA with a length of more than 200 nucleotides. It plays an important role in ALI, cell cycle regulation, cell differentiation regulation, and many other life activities. Therefore, the current focus is to identify and evaluate the possible functions and potential molecular mechanisms of lncRNA small nuclear host gene 12 (SNHG12). Lipopolysaccharide (LPS)-induced mice model and in vitro cell model were established. Gene knockout is to use the principle of DNA homologous recombination to replace the target gene fragment with the designed homologous fragment, so as to achieve the purpose of gene knockout. The relationship between lncRNA SNHG12 expression and ALI was studied through knockdown and overexpression experiments. The qRT-PCR, ROS, immunohistochemistry, histopathology, TUNEL, and cell permeability tests were performed to further verify the possible targets and mechanisms of action. The expression of lncRNA SNHG12 in lung tissue was lower than that in normal tissue. The results showed that lncRNA SNHG12 could reduce lung cell injury and inflammatory cytokines induced by ALI. Bioinformatics analysis showed that lncRNA SNHG12 interacted with miR-140-3p. Subsequent experiments confirmed the link between lncRNA SNHG12, miR-140-3p, and fndc5. Furthermore, this study indicates that lncRNA SNHG12 has a key function in ALI. The results of this study demonstrated the role of lncRNA SNHG12 in the pathological process of ALI and provided a reference for developing novel anti-ALI treatments so that patients can get timely treatment, avoid causing multiple organ failure, and will not endanger their life safety.

An Integrated Database of Small RNAs and Their Interplay With Transcriptional Gene Regulatory Networks in Corynebacteria

Small RNAs (sRNAs) are one of the key players in the post-transcriptional regulation of bacterial gene expression. These molecules, together with transcription factors, form regulatory networks and greatly influence the bacterial regulatory landscape. Little is known concerning sRNAs and their influence on the regulatory machinery in the genus Corynebacterium, despite its medical, veterinary and biotechnological importance. Here, we expand corynebacterial regulatory knowledge by integrating sRNAs and their regulatory interactions into the transcriptional regulatory networks of six corynebacterial species, covering four human and animal pathogens, and integrate this data into the CoryneRegNet database. To this end, we predicted sRNAs to regulate 754 genes, including 206 transcription factors, in corynebacterial gene regulatory networks. Amongst them, the sRNA Cd-NCTC13129-sRNA-2 is predicted to directly regulate ydfH, which indirectly regulates 66 genes, including the global regulator glxR in C. diphtheriae. All of the sRNA-enriched regulatory networks of the genus Corynebacterium have been made publicly available in the newest release of CoryneRegNet(www.exbio.wzw.tum.de/coryneregnet/) to aid in providing valuable insights and to guide future experiments.

Are spliced ncRNA host genes distinct classes of lncRNAs?

Many small nucleolar RNAs and many of the hairpin precursors of miRNAs are processed from long non-protein-coding host genes. In contrast to their highly conserved and heavily structured payload, the host genes feature poorly conserved sequences. Nevertheless, there is mounting evidence that the host genes have biological functions beyond their primary task of carrying a ncRNA as payload. So far, no connections between the function of the host genes and the function of their payloads have been reported. Here we investigate whether there is evidence for an association of host gene function or mechanisms with the type of payload. To assess this hypothesis we test whether the miRNA host genes (MIRHGs), snoRNA host genes (SNHGs), and other lncRNA host genes can be distinguished based on sequence and/or structure features unrelated to their payload. A positive answer would imply a functional and mechanistic correlation between host genes and their payload, provided the classification does not depend on the presence and type of the payload. A negative answer would indicate that to the extent that secondary functions are acquired, they are not strongly constrained by the prior, primary function of the payload. We find that the three classes can be distinguished reliably when the classifier is allowed to extract features from the payloads. They become virtually indistinguishable, however, as soon as only sequence and structure of parts of the host gene distal from the snoRNAs or miRNA payload is used for classification. This indicates that the functions of MIRHGs and SNHGs are largely independent of the functions of their payloads. Furthermore, there is no evidence that the MIRHGs and SNHGs form coherent classes of long non-coding RNAs distinguished by features other than their payloads.

LncRNA expression profile during autophagy and Malat1 function in macrophages

Long noncoding RNAs (lncRNAs) are a class of functional non-coding transcripts that are longer than 200 nt and regulate gene expression via diverse mechanisms in eukaryotes. In fact, they have emerged as critical epigenetic and transcriptional regulators of autophagy in mammals in response to various stressors. Autophagy not only plays a crucial role in maintaining cellular homeostasis, but it is also essential to immunity, targets intracellular pathogens for degradation, modulates inflammation, and participates in adaptive immune responses. However, the expression profile of lncRNA and its role in regulating autophagy in macrophages have been poorly defined. Here, we used transcriptomic and bioinformatics to analysis LncRNA expression profile during autophagy and functional studies to evaluate the function of the metastasis-associated lung adenocarcinoma transcript-1 (Malat1) lncRNA in macrophages. A total of 1112 putative lncRNAs (240 novel lncRNAs) were identified, including 831 large intergenic, 129 intronic, and 152 anti-sense lncRNA, of which 59 differentially expressed transcripts exhibited a greater than 1.5-fold change under different conditions. The interaction of Malat1 lncRNA with microRNA (mir)-23-3p and lysosomal-associated membrane protein 1 (Lamp1) was found, Malat1 releases inhibition of Lamp1 expression in macrophages through competitive adsorption of mir-23-3p. The results of this study provide a better understanding of lncRNA function in macrophages and a basis for further investigation into the roles and mechanisms of ncRNA in immunology, particularly the functions of Malat1 and mir-23-3p in the pathogenesis of macrophages.

Computational analysis of non-coding RNAs in Alzheimer's disease

Latest studies have shown that Long Noncoding RNAs corresponds to a crucial factor in neurodegenerative diseases and next-generation therapeutic targets. A wide range of advanced computational methods for the analysis of Noncoding RNAs mainly includes the prediction of RNA and miRNA structures. The problems that concern representations of specific biological structures such as secondary structures are either characterized as NP-complete or with high complexity. Numerous algorithms and techniques related to the enumeration of sequential terms of biological structures and mainly with exponential complexity have been constructed until now. While BACE1-AS, NATRad18, 17A, and hnRNP Q lnRNAs have been found to be associated with Alzheimer's disease, in this research study the significance of the most known β-turn-forming residues between these proteins is computationally identified and discussed, as a potentially crucial factor on the regulation of folding, aggregation and other intermolecular interactions.

Challenges and approaches to predicting RNA with multiple functional structures

The revolution in sequencing technology demands new tools to interpret the genetic code. As in vivo transcriptome-wide chemical probing techniques advance, new challenges emerge in the RNA folding problem. The emphasis on one sequence folding into a single minimum free energy structure is fading as a new focus develops on generating RNA structural ensembles and identifying functional structural features in ensembles. This review describes an efficient combinatorially complete method and three free energy minimization approaches to predicting RNA structures with more than one functional fold, as well as two methods for analysis of a thermodynamics-based Boltzmann ensemble of structures. The review then highlights two examples of viral RNA 3'-UTR regions that fold into more than one conformation and have been characterized by single molecule fluorescence energy resonance transfer or NMR spectroscopy. These examples highlight the different approaches and challenges in predicting structure and function from sequence for RNA with multiple biological roles and folds. More well-defined examples and new metrics for measuring differences in RNA structures will guide future improvements in prediction of RNA structure and function from sequence.