Protocol for identifying differentially expressed genes using the RumBall RNA-seq analysis platform.

Here, we present a protocol for the identification of differentially expressed genes through RNA sequencing analysis. Starting with FASTQ files from public datasets, this protocol leverages RumBall within a self-contained Docker system. We describe the steps for software setup, obtaining data, read mapping, sample normalization, statistical modeling, and gene ontology enrichment. We then detail procedures for interpreting results with plots and tables. RumBall internally utilizes popular tools, ensuring a comprehensive understanding of the analysis process.

Nutrient-driven dedifferentiation of enteroendocrine cells promotes adaptive intestinal growth in Drosophila.

Post-developmental organ resizing improves organismal fitness under constantly changing nutrient environments. Although stem cell abundance is a fundamental determinant of adaptive resizing, our understanding of its underlying mechanisms remains primarily limited to the regulation of stem cell division. Here, we demonstrate that nutrient fluctuation induces dedifferentiation in the Drosophila adult midgut to drive adaptive intestinal growth. From lineage tracing and single-cell RNA sequencing, we identify a subpopulation of enteroendocrine (EE) cells that convert into functional intestinal stem cells (ISCs) in response to dietary glucose and amino acids by activating the JAK-STAT pathway. Genetic ablation of EE-derived ISCs severely impairs ISC expansion and midgut growth despite the retention of resident ISCs, and in silico modeling further indicates that EE dedifferentiation enables an efficient increase in the midgut cell number while maintaining epithelial cell composition. Our findings identify a physiologically induced dedifferentiation that ensures ISC expansion during adaptive organ growth in concert with nutrient conditions.

Context-dependent perturbations in chromatin folding and the transcriptome by cohesin and related factors.

Cohesin regulates gene expression through context-specific chromatin folding mechanisms such as enhancer-promoter looping and topologically associating domain (TAD) formation by cooperating with factors such as cohesin loaders and the insulation factor CTCF. We developed a computational workflow to explore how three-dimensional (3D) structure and gene expression are regulated collectively or individually by cohesin and related factors. The main component is CustardPy, by which multi-omics datasets are compared systematically. To validate our methodology, we generated 3D genome, transcriptome, and epigenome data before and after depletion of cohesin and related factors and compared the effects of depletion. We observed diverse effects on the 3D genome and transcriptome, and gene expression changes were correlated with the splitting of TADs caused by cohesin loss. We also observed variations in long-range interactions across TADs, which correlated with their epigenomic states. These computational tools and datasets will be valuable for 3D genome and epigenome studies.

Analyzing the 3D chromatin organization coordinating with gene expression regulation in B-cell lymphoma.

BACKGROUND: Eukaryotes compact chromosomes densely and non-randomly, forming three-dimensional structures. Alterations of the chromatin structures are often associated with diseases. In particular, aggressive cancer development from the disruption of the humoral immune system presents abnormal gene regulation which is accompanied by chromatin reorganizations. How the chromatin structures orchestrate the gene expression regulation is still poorly understood. Herein, we focus on chromatin dynamics in normal and abnormal B cell lymphocytes, and investigate its functional impact on the regulation of gene expression. METHODS: We conducted an integrative analysis using publicly available multi-omics data that include Hi-C, RNA-seq and ChIP-seq experiments with normal B cells, lymphoma and ES cells. We processed and re-analyzed the data exhaustively and combined different scales of genome structures with transcriptomic and epigenetic features. RESULTS: We found that the chromatin organizations are highly preserved among the cells. 5.2% of genes at the specific repressive compartment in normal pro-B cells were switched to the permissive compartment in lymphoma along with increased gene expression. The genes are involved in B-cell related biological processes. Remarkably, the boundaries of topologically associating domains were not enriched by CTCF motif, but significantly enriched with Prdm1 motif that is known to be the key factor of B-cell dysfunction in aggressive lymphoma. CONCLUSIONS: This study shows evidence of a complex relationship between chromatin reorganization and gene regulation. However, an unknown mechanism may exist to restrict the structural and functional changes of genomic regions and cognate genes in a specific manner. Our findings suggest the presence of an intricate crosstalk between the higher-order chromatin structure and cancer development.

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.

MOTIVATION: As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction-modification systems. For better understanding of their functional mechanisms, it is fundamentally important to identify 4mC modification. Machine learning methods have recently emerged as an effective and efficient approach for the high-throughput identification of 4mC sites, although high predictive error rates are still challenging for existing methods. Therefore, it is highly desirable to develop a computational method to more accurately identify m4C sites. RESULTS: In this study, we propose a machine learning based predictor, namely 4mcPred-SVM, for the genome-wide detection of DNA 4mC sites. In this predictor, we present a new feature representation algorithm that sufficiently exploits sequence-based information. To improve the feature representation ability, we use a two-step feature optimization strategy, thereby obtaining the most representative features. Using the resulting features and Support Vector Machine (SVM), we adaptively train the optimal models for different species. Comparative results on benchmark datasets from six species indicate that our predictor is able to achieve generally better performance in predicting 4mC sites as compared to the state-of-the-art predictors. Importantly, the sequence-based features can reliably and robust predict 4mC sites, facilitating the discovery of potentially important sequence characteristics for the prediction of 4mC sites. AVAILABILITY AND IMPLEMENTATION: The user-friendly webserver that implements the proposed 4mcPred-SVM is well established, and is freely accessible at http://server.malab.cn/4mcPred-SVM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.