Cell-fate conversion of intestinal cells in adult Drosophila midgut by depleting a single transcription factor

The manipulation of cell identity by reprograming holds immense potential in regenerative medicine, but is often limited by the inefficient acquisition of fully functional cells. This problem can potentially be resolved by better understanding the reprogramming process using in vivo genetic models, which are currently scarce. Here we report that both enterocytes (ECs) and enteroendocrine cells (EEs) in adult Drosophila midgut show a surprising degree of cell plasticity. Depleting the transcription factor Tramtrack in the differentiated ECs can initiate Prospero-mediated cell transdifferentiation, leading to EE-like cells. On the other hand, depletion of Prospero in the differentiated EEs can lead to the loss of EE-specific transcription programs and the gain of intestinal progenitor cell identity, allowing cell cycle re-entry or differentiation into ECs. We find that intestinal progenitor cells, ECs, and EEs have a similar chromatin accessibility profile, supporting the concept that cell plasticity is enabled by pre-existing chromatin accessibility with switchable transcription programs. Further genetic analysis with this system reveals that the NuRD chromatin remodeling complex, cell lineage confliction, and age act as barriers to EC-to-EE transdifferentiation. The establishment of this genetically tractable in vivo model should facilitate mechanistic investigation of cell plasticity at the molecular and genetic level.

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Rongwen Xi Xi
Mar 5, 5, 2024 NIKON NIS Elements (version 5.21.00);BD BD FACSDiva (version 6.1.3)Bulk RNA-sequencing: Raw reads were mapped to to D. D. melanogaster genome (BDGP6) and counts assigned to to protein-coding genes were calculated using featureCounts (v1.6.3).DESeq2 was then used to to identify significantly differently expressed genes using the following parameters: Padj<0.01, and the absolute value of of log2 FC>0.5.GO GO analysis for differently expressed genes was performed using DAVID 42, and the R package "pheatmap" was used for generating heatmaps.ATAC-seq analysis: Raw sequencing reads were mapped to to the D. D. melanogaster genome (BDGP6) using bowtie2 (version 2.2.4).To To generate bigWig (bw) files, the deepTools bamCoverage function was employed with BPM normalization.Finally, peaks were called using MACS3 to to identify regions of of enriched signal.The GSEA analysis was performed done using the R package "clusterprofiler"; ImageJ software (version 1.48v) was utilized for cell number counting.GraphPad Prism 6 software (GraphPad Software Inc.) was used to to calculate p-values by by unpaired student's t-test or or anova test.

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The raw and processed datasets, including RNA-seq data and ATAC-seq results generated in this study, have been made available in the supplementary material or deposited in the GEO database under the accession code GSE235505 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE235505).The genome datasets used in this study was BDGP6 for RNA-seq and ATAC-seq analysis.Additionally, three RNA-seq datasets previously reported by our lab are accessible in the GEO database under the following accession codes: GSE130943 (RNA-seq data of esg+ cell; https://www.No data were excluded from these analysis.
All experiments were reproduced for at least 2-3 times and representative results were shown in the manuscript.For key results, independent RNAi lines targeting to the same genes were also used to verify these findings.
The flies with same genotypes were collected together and been randomly allocated into different groups or treatments.
Blinding was performed in all quantifications.Other experiments were not blinded to the investigators, as the genotypes of the fly strains should be visible to the investigators to carry out appropriate treatment, and staining with appropriate antibody .