Biology subjects

Manosalva Perez, N.

Publications and source records attributed to Manosalva Perez, N..

3 recordsLinked to original sources

bioRxiv2023

MINI-AC: Inference of plant gene regulatory networks using bulk or single-cell accessible chromatin profiles

Gene regulatory networks (GRNs) represent the interactions between transcription factors (TF) and their target genes. GRNs control transcriptional programs involved in growth, development and stress responses, ultimately affecting diverse agricultural traits. While recent developments in accessible chromatin (AC) profiling technologies make it possible to identify context-specific regulatory DNA, learning the underlying GRNs remains a major challenge. We developed MINI-AC (Motif-Informed Network Inference based on Accessible Chromatin), a method that combines AC data from bulk or single-cell experiments with TF binding site information to learn GRNs in plants. We benchmarked MINI-AC using bulk AC datasets from different Arabidopsis thaliana tissues and showed that it outperforms other methods to identify correct TFs binding sites. In maize, a crop with a complex genome and abundant distal AC regions, MINI-AC successfully inferred leaf GRNs with experimentally confirmed, both proximal and distal, TF-target gene interactions. Furthermore, we showed that both AC regions and footprints are valid alternatives to infer AC-based GRNs with MINI-AC. Finally, we combined MINI-AC predictions from bulk and single-cell AC datasets to identify general and cell-type specific maize leaf regulators. Focusing on C4 metabolism, we identified diverse regulatory interactions in specialized cell types for this photosynthetic pathway. MINI-AC represents a powerful tool for inferring accurate AC-derived GRNs in plants and identifying known and novel candidate regulators, improving our understanding of gene regulation in plants.

bioinformatics↗

bioRxiv2022

Regulatory and evolutionary annotation identifies KAN1, MYB44 and PIF4 as regulators of Arabidopsis lincRNAs expressed in root

Thousands of long intergenic noncoding RNAs (lincRNAs) have been identified in plant genomes. While some lincRNAs have been characterized as important regulators in different biological processes, little is known about the transcriptional regulation for most plant lincRNAs. Through the integration of eight annotation resources, we defined 6,599 high-confidence lincRNA loci in Arabidopsis thaliana. For lincRNAs belonging to different evolutionary age categories, we identified major differences in sequence and chromatin features, as well as in the level of conservation and purifying selection acting during evolution. Spatiotemporal gene expression profiles combined with transcription factor (TF) chromatin immunoprecipitation data were used to construct a TF- lincRNA regulatory network containing 2,659 lincRNAs and 15,686 interactions. We found that properties characterizing lincRNA expression, conservation and regulation differ between plants and animals. Experimental validation confirmed the role of three TFs, KAN1, MYB44, and PIF4, as key regulators controlling root- specific lincRNA expression, demonstrating the predictive power of our network. Furthermore, we identified 58 lincRNAs, regulated by these TFs, showing strong root cell-type specific expression or chromatin accessibility, which are linked with GWAS genetic associations related to root system development and growth. The multi-level genome-wide characterization covering chromatin state information, promoter conservation, and ChIP-based TF binding, for all detectable lincRNAs across 769 expression samples, permits to rapidly define the biological context and relevance of lincRNAs in Arabidopsis regulatory networks. One-line sentenceA multi-level Arabidopsis gene regulatory network identifies novel regulators controlling root-specific lincRNA expression, offering a promising strategy to identify lincRNAs involved in plant biology.

plant biology↗

bioRxiv2022

MINI-EX: Integrative inference of single-cell gene regulatory networks in plants

Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factor (TF) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell-types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell Expression data), an integrative approach to infer cell-type specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell-types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell-types controlling a biological process of interest. We demonstrate MINI-EXs stability towards input data sets with low number of cells and its robustness towards missing data, and we show it infers state-of-the-art networks with a better performance compared to related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, Arabidopsis leaf development, and governing ear development in maize, enhancing our understanding of cell-type specific regulation and unraveling the role of different regulators controlling the development of specific cell-types in plants.

bioinformatics↗