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RNA splicing plays a critical role in post-transcriptional gene regulation. Exponential expansion of intron length poses a challenge for accurate splicing. Little is known about how cells prevent inadvertent and often deleterious expression of intronic elements due to cryptic splicing. In this study, we identify hnRNPM as an essential RNA binding protein that suppresses cryptic splicing through binding to deep introns, preserving transcriptome integrity. Long interspersed nuclear elements (LINEs) harbor large amounts of pseudo splice sites in introns. hnRNPM preferentially binds at intronic LINEs and represses LINE-containing pseudo splice site usage for cryptic splicing. Remarkably, a subgroup of the cryptic exons can form long dsRNAs through base-pairing of inverted Alu transposable elements scattered in between LINEs and trigger interferon immune response, a well-known antiviral defense mechanism. Notably, these interferon-associated pathways are found to be upregulated in hnRNPM-deficient tumors, which also exhibit elevated immune cell infiltration. These findings unveil hnRNPM as a guardian of transcriptome integrity. Targeting hnRNPM in tumors may be used to trigger an inflammatory immune response thereby boosting cancer surveillance.

Alternative splicing is an essential mechanism of gene regulation, giving rise to remarkable protein diversity in higher eukaryotes. Epithelial-mesenchymal transition (EMT) is a developmental process that plays an essential role in metazoan embryogenesis. Recent studies have revealed that alternative splicing serves as a fundamental layer of regulation that governs cells to undergo EMT. In this review, we summarize recent findings on the functional impact of alternative splicing in EMT and EMT-associated activities. We then discuss the regulatory mechanisms that control alternative splicing changes during EMT.

During breast cancer metastasis, the developmental process epithelial–mesenchymal transition (EMT) is abnormally activated. Transcriptional regulatory networks controlling EMT are well-studied; however, alternative RNA splicing also plays a critical regulatory role during this process. A comprehensive understanding of alternative splicing (AS) and the RNA binding proteins (RBPs) that regulate it during EMT and their impact on breast cancer remains largely unknown. In this study, we annotated AS in the breast cancer TCGA data set and identified an AS signature that is capable of distinguishing epithelial and mesenchymal states of the tumors. This AS signature contains 25 AS events, among which nine showed increased exon inclusion and 16 showed exon skipping during EMT. This AS signature accurately assigns the EMT status of cells in the CCLE data set and robustly predicts patient survival. We further developed an effective computational method using bipartite networks to identify RBP-AS networks during EMT. This network analysis revealed the complexity of RBP regulation and nominated previously unknown RBPs that regulate EMT-associated AS events. This study highlights the importance of global AS regulation during EMT in cancer progression and paves the way for further investigation into RNA regulation in EMT and metastasis.

Alternative splicing has been shown to causally contribute to the epithelial–mesenchymal transition (EMT) and tumor metastasis. However, the scope of splicing factors that govern alternative splicing in these processes remains largely unexplored. Here we report the identification of A-Kinase Anchor Protein (AKAP8) as a splicing regulatory factor that impedes EMT and breast cancer metastasis. AKAP8 not only is capable of inhibiting splicing activity of the EMT-promoting splicing regulator hnRNPM through protein–protein interaction, it also directly binds to RNA and alters splicing outcomes. Genome-wide analysis shows that AKAP8 promotes an epithelial cell state splicing program. Experimental manipulation of an AKAP8 splicing target CLSTN1 revealed that splice isoform switching of CLSTN1 is crucial for EMT. Moreover, AKAP8 expression and the alternative splicing of CLSTN1 predict breast cancer patient survival. Together, our work demonstrates the essentiality of RNA metabolism that impinges on metastatic breast cancer., Splice isoform switching regulated by the heterogeneous nuclear ribonucleoprotein M (hnRNPM) induces EMT and metastasis. Here, the authors report that AKAP8 is a metastasis suppressor that inhibits the splicing activity of hnRNPM and antagonizes genome-wide EMT-associated alternative splicing to maintain epithelial cell state.

Silicon cutting waste (SCW) generated during Si wafers producing process can be recycled by Al Si alloying process. However, the presence of O in SCW has a detrimental impact on recycling process. In this study, cryolite was introduced to eliminate the hindrance of O. The influences of smelting temperature and the amount of cryolite additive on the yield of the blocky Al Si alloys and the Si recovery ratio of the SCW have been investigated and the alloying conditions were optimized to a smelting temperature of 1000 °C and a cryolite/SCW mass ratio of 0.8, achieving a Al Si alloys yield of 95.99% and a Si recovery ratio of 84.77%, which were far greater than those without cryolite additive. The results showed that the addition of cryolite additive can effectively improve the smelting effect and reduce the alloying temperature. Furthermore, the action mechanism of cryolite in Al-SCW system was analyzed, and the results revealed that the molten cryolite can dissolve the generated Al2O3 existing on the surface of Al Si alloy droplets and finally contributes to the aggregation of these droplets. This method has advantages including high Si recovery ratio of SCW, low alloying temperature and simple technological process.

Quantification of alternative splicing to detect the abundance of differentially spliced isoforms of a gene in total RNA can be accomplished via RT-PCR using both quantitative real-time and semi-quantitative PCR methods. These methods require careful PCR primer design to ensure specific detection of particular splice isoforms. We will also describe analysis of alternative splicing using a splicing “minigene” in mammalian cell tissue culture to facilitate investigation of the regulation of alternative splicing of a particular exon of interest.

Motivation: DNA methylation is a biological process impacting the gene functions without changing the underlying DNA sequence. The DNA methylation machinery usually attaches methyl groups to some specific cytosine residues, which modify the chromatin architectures. Such modifications in the promoter regions will inactivate some tumor-suppressor genes. DNA methylation within the coding region may significantly reduce the transcription elongation efficiency. The gene function may be tuned through some cytosines are methylated. Methods: This study hypothesizes that the overall methylation level across a gene may have a better association with the sample labels like diseases than the methylations of individual cytosines. The gene methylation level is formulated as a regression model using the methylation levels of all the cytosines within this gene. A comprehensive evaluation of various feature selection algorithms and classification algorithms is carried out between the gene-level and residue-level methylation levels. Results: A comprehensive evaluation was conducted to compare the gene and cytosine methylation levels for their associations with the sample labels and classification performances. The unsupervised clustering was also improved using the gene methylation levels. Some genes demonstrated statistically significant associations with the class label, even when no residue-level methylation features have statistically significant associations with the class label. So in summary, the trained gene methylation levels improved various methylome-based machine learning models. Both methodology development of regression algorithms and experimental validation of the gene-level methylation biomarkers are worth of further investigations in the future studies. The source code, example data files and manual are available at http://www.healthinformaticslab.org/supp/.

Soil organic matters (SOM), specifically carbon and nitrogen, bring numerous benefits to soil’s physical and chemical properties. In this paper, we employ spectral data obtained by Fourier transform near-infrared (FT-NIR) spectroscopy to predict the content of organic carbon (OC) and total nitrogen (TN) in mineral soils. To address the limitation generated by massive hyperparameters on convolution neural network (CNN), we substitute using a technique named SVD concatenation to learn features. The proposed model combines the layers of fully connected and regression to complete the prediction task. We abbreviate it as SVD-CNN, which is capable provide a multi-tasks output simultaneously. In experiments, we study the prediction performances of SVD-CNN on two datasets of FT-NIR and LUCAS 2009 topsoil. Based on different situations, the highest performance of R2 achieves 0.8891 for OC and 0.9048 for TN on the FT-NIR dataset. Similarly, the most prominent results on the LUCAS 2009 topsoil dataset are R2 = 0.9304, RMSE = 3.6014 for OC and R2 = 0.9319, RMSE = 0.2733 for TN. Furthermore, we also evaluate the results obtained by solely using SVD concatenation, which reveals SVD-CNN performs a better generalization ability.