Gitau, J. | bioRxiv Science

bioRxiv2026

Drought reshapes enhancer-like nascent transcription and gene regulation in Oryza sativa

Drought increasingly constrains global rice productivity, yet how water deficit remodels cis-regulatory activity in plants remains poorly resolved. Here we used precision run-on sequencing (PRO-seq) to profile nascent transcription in rice leaves under well-watered and drought conditions and mapped transcription-initiation regions with the tool dREG, which detects genome-wide peaks of bidirectional transcription displaying active-enhancer behaviour. PRO-seq captured a robust drought response at genes and revealed extensive remodelling of initiation landscapes. We detected 85,764 consensus dREG sites, of which 17,193 changed significantly under drought and were predominantly intergenic. Because plant intergenic space is rich in transposable elements and silencing-associated transcription, we integrated transposable-element overlap and small-RNA loci with chromatin accessibility and DNA methylation to prioritize 2,428 drought-responsive intergenic sites (841 induced and 1,308 repressed) that are accessible, locally hypomethylated, and bidirectionally transcribed - features consistent with enhancer-like elements. Activity at proximal candidates correlated with elevated nascent transcription of nearby genes, and a subset overlapped gene-connected chromatin loop anchors, supporting candidate enhancer-target relationships. Motif enrichment further supported the involvement of drought-responsive regulatory programs, and hundreds of candidates overlapped rice STARR-seq enhancers. Together, these data define a drought-responsive atlas of candidate enhancer-like nascent transcription in rice and provide prioritized cis-regulatory candidates for mechanistic validation and crop improvement.

plant biology↗

bioRxiv2025

Mutational and Expression Profile of ZNF217, ZNF750, ZNF703 Zinc Finger Genes in Kenya Women diagnosed with Breast Cancer

ObjectiveTo characterize the mutational landscape and expression profiles of ZNF217, ZNF703, and ZNF750, and assess their clinical relevance in breast cancer patients from Kenya. MethodsWhole-exome sequencing (WES) and RNA sequencing (RNA-Seq) data from 23 paired tumor-normal samples were analyzed in a Linux-based environment. Somatic mutations were identified using MuTect2 following alignment to the hg38 reference genome and annotation with VEP. Variants were classified by type, coding consequence, and protein position, and mapped to functional domains. Recurrent mutations were identified, and comparisons were made with The Cancer Genome Atlas (TCGA). Gene expression was quantified using STAR and featureCounts, normalized with DESeq2, and analyzed using paired statistical tests with multiple testing correction. Principal component analysis (PCA) and regression analyses were performed to assess expression patterns and clinical associations. ResultsZNF217 and ZNF750 exhibited high mutational burdens, whereas ZNF703 showed a lower mutation frequency. Mutations were predominantly single nucleotide variants, with missense and synonymous variants as the major classes. Variants were distributed across protein sequences, with limited domain enrichment and no clear hotspot clustering. Recurrent mutations were gene-specific and infrequent. Comparison with TCGA data showed concordant mutation prevalence for ZNF217, low frequency for ZNF703, and absence of ZNF750 mutations. All three genes were significantly upregulated in tumors compared to matched normal tissues (ZNF217: p = 0.00068; ZNF703: p = 0.00475; ZNF750: p = 0.00366). Tumor expression exceeded normal expression in 74% of cases for ZNF217, 64% for ZNF703, and 83% for ZNF750. PCA demonstrated partial separation between tumor and normal samples. ZNF703 expression was positively associated with body mass index ({beta} = 0.194, p = 0.025), and ZNF750 expression was higher in estrogen receptor-positive tumors ({beta} = 1.050, p = 0.005). ConclusionZNF217, ZNF703, and ZNF750 display distinct mutation and expression profiles in breast cancer, with evidence of cohort-specific variation. These findings highlight gene-specific mechanisms of dysregulation and emphasize the value of integrating genomic and transcriptomic analyses.

cancer biology↗