The dynamic landscape of enhancer-derived RNA during mouse early embryo development.

Enhancer-derived RNAs (eRNAs) play critical roles in diverse biological processes by facilitating their target gene expression. However, the abundance and function of eRNAs in early embryos are not clear. Here, we present a comprehensive eRNA atlas by systematically integrating publicly available datasets of mouse early embryos. We characterize the transcriptional and regulatory network of eRNAs and show that different embryo developmental stages have distinct eRNA expression and regulatory profiles. Paternal eRNAs are activated asymmetrically during zygotic genome activation (ZGA). Moreover, we identify an eRNA, MZGAe1, which plays an important function in regulating mouse ZGA and early embryo development. MZGAe1 knockdown leads to a developmental block from 2-cell embryo to blastocyst. We create an online data portal, M2ED2, to query and visualize eRNA expression and regulation. Our study thus provides a systematic landscape of eRNA and reveals the important role of eRNAs in regulating mouse early embryo development.

Ribosomal RNA regulates chromosome clustering during mitosis.

Noncoding RNAs are known to associate with mitotic chromosomes, but the identities and functions of chromosome-associated RNAs in mitosis remain elusive. Here, we show that rRNA species associate with condensed chromosomes during mitosis. In particular, pre-rRNAs such as 45S, 32S, and 30S are highly enriched on mitotic chromosomes. Immediately following nucleolus disassembly in mitotic prophase, rRNAs are released and associate with and coat each condensed chromosome at prometaphase. Using unbiased mass spectrometry analysis, we further demonstrate that chromosome-bound rRNAs are associated with Ki-67. Moreover, the FHA domain and the repeat region of Ki-67 recognize and anchor rRNAs to chromosomes. Finally, suppression of chromosome-bound rRNAs by RNA polymerase I inhibition or by using rRNA-binding-deficient Ki-67 mutants impair mitotic chromosome dispersion during prometaphase. Our study thus reveals an important role of rRNAs in preventing chromosome clustering during mitosis.

PALP: A rapid imaging technique for stratifying ferroptosis sensitivity in normal and tumor tissues in situ.

Ferroptosis is an emerging cancer suppression strategy. However, how to select cancer patients for treating with ferroptosis inducers remains challenging. Here, we develop photochemical activation of membrane lipid peroxidation (PALP), which uses targeted lasers to induce localized polyunsaturated fatty acyl (PUFA)-lipid peroxidation for reporting ferroptosis sensitivity in cells and tissues. PALP captured by BODIPY-C11 can be suppressed by lipophilic antioxidants and iron chelation, and is dependent on PUFA-lipid levels. Moreover, we develop PALPv2, for studying lipid peroxidation on selected membranes along the z axis in live cells using two-photon microscopes. Using PALPv1, we detect PUFA-lipids in multiple tissues, and validate a PUFA-phospholipid reduction during muscle aging as previously reported. Patterns of PALPv1 signals across multiple cancer cell types in vitro and in vivo are concordant with their ferroptosis susceptibility and PUFA-phospholipid levels. We envision that PALP will enable rapid stratification of ferroptosis sensitivity in cancer patients and facilitate PUFA-lipid research.

Gene Selection for the Discrimination of Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) is the third most common cancer worldwide. Cancer discrimination is a typical application of gene expression analysis using a microarray technique. However, microarray data suffer from the curse of dimensionality and usual imbalanced class distribution between the majority (tumor samples) and minority (normal samples) classes. Feature gene selection is necessary and important for cancer discrimination. OBJECTIVES: To select feature genes for the discrimination of CRC. METHODS: We improve the feature selection algorithm based on differential evolution, DEFSw by using RUSBoost classifier and weight accuracy instead of the common classifier and evaluation measure for selecting feature genes from imbalance data. We firstly extract differently expressed genes (DEGs) from the CRC dataset of the TCGA and then select the feature genes from the DEGs using the improved DEFSw algorithm. Finally, we validate the selected feature gene sets using independent datasets and retrieve the cancer related information for these genes based on text mining through the Coremine Medical online database. RESULTS: We select out 16 single-gene feature sets for colorectal cancer discrimination and 19 single-gene feature sets only for colon cancer discrimination. CONCLUSIONS: In summary, we find a series of high potential candidate biomarkers or signatures, which can discriminate either or both of colon cancer and rectal cancer with high sensitivity and specificity.

An Integrated Approach for Identifying Molecular Subtypes in Human Colon Cancer Using Gene Expression Data.

Identifying molecular subtypes of colorectal cancer (CRC) may allow for more rational, patient-specific treatment. Various studies have identified molecular subtypes for CRC using gene expression data, but they are inconsistent and further research is necessary. From a methodological point of view, a progressive approach is needed to identify molecular subtypes in human colon cancer using gene expression data. We propose an approach to identify the molecular subtypes of colon cancer that integrates denoising by the Bayesian robust principal component analysis (BRPCA) algorithm, hierarchical clustering by the directed bubble hierarchical tree (DBHT) algorithm, and feature gene selection by an improved differential evolution based feature selection method (DEFSW) algorithm. In this approach, the normal samples being completely and exclusively clustered into one class is considered to be the standard of reasonable clustering subtypes, and the feature selection pays attention to imbalances of samples among subtypes. With this approach, we identified the molecular subtypes of colon cancer on the mRNA gene expression dataset of 153 colon cancer samples and 19 normal control samples of the Cancer Genome Atlas (TCGA) project. The colon cancer was clustered into 7 subtypes with 44 feature genes. Our approach could identify finer subtypes of colon cancer with fewer feature genes than the other two recent studies and exhibits a generic methodology that might be applied to identify the subtypes of other cancers.