Biology subjects

Mejia Guerra, M. K.

Publications and source records attributed to Mejia Guerra, M. K..

4 recordsLinked to original sources

bioRxiv2020

Cis-regulatory elements within TEs can influence expression of nearby maize genes

Transposable elements (TEs) have the potential to create regulatory variation both through disruption of existing DNA regulatory elements and through creation of novel DNA regulatory elements. In a species with a large genome, such as maize, the many TEs interspersed with genes creates opportunities for significant allelic variation due to TE presence/absence polymorphisms among individuals. We used information on putative regulatory elements in combination with knowledge about TE polymorphisms in maize to identify TE insertions that interrupt existing accessible chromatin regions (ACRs) in B73 as well as examples of polymorphic TEs that contain ACRs among four inbred lines of maize including B73, Mo17, W22, and PH207. The TE insertions in three other assembled maize genomes (Mo17, W22 or PH207) that interrupt ACRs that are present in the B73 genome can trigger changes to the chromatin suggesting the potential for both genetic and epigenetic influences of these insertions. Nearly 20% of the ACRs located over 2kb from the nearest gene are located within an annotated TE. These are regions of unmethylated DNA that show evidence for functional importance similar to ACRs that are not present within TEs. Using a large panel of maize genotypes we tested if there is an association between the presence of TE insertions that interrupt, or carry, an ACR and the expression of nearby genes. TEs that carry ACRs exhibit an enrichment for being associated with higher expression of nearby genes, suggesting that these TEs may create novel regulatory elements. These analyses highlight the potential for TEs to rewire transcriptional responses in eukaryotic genomes. Data AvailabilityIn this study we utilize previously published datasets that are available through the following accessions: SRX4727413, SRR8738272, and SRR8740852.

plant biology↗

bioRxiv2020

Decoding the regulatory architecture of the maize leaf

The transcription regulatory network underlying essential and complex functionalities inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex regulatory netowrk. Here, we used ChIP-seq to determine the binding profiles of 104 TF expressed in the maize leaf. With this large dataset, we could reconstruct a transcription regulatory network that covers over 77% of the expressed genes, and reveal its scale-free topology and functional modularity like a real-world network. We found that TF binding occurs in clusters covering [~]2% of the genome, and shows enrichment for sequence variations associated with eQTLs and GWAS hits of complex agronomic traits. Machine-learning analyses were used to identify TF sequence preferences, and showed that co-binding is key for TF specificity. The trained models were used to predict and compare the regulatory networks in other species and showed that the core network is evolutionarily conserved. This study provided an extensive description of the architecture, organizing principle and evolution of the transcription regulatory network inside the plant leaf.

molecular biology↗

bioRxiv2018

Evolutionarily informed deep learning methods: Predicting transcript abundance from DNA sequence

Deep learning methodologies have revolutionized prediction in many fields, and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two novel approaches that account for evolutionary relatedness in machine learning models: 1) gene-family guided splitting, and 2) ortholog contrasts. The first approach accounts for evolution by constraining the models training and testing sets to include different gene families. The second, uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction, and have prediction auROC values ranging from 0.72 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the novel hypothesis that the 3 UTR is more important for fine tuning mRNA abundance levels while the 5 UTR is more important for large scale changes.

molecular biology↗

bioRxiv2017

k-mer grammar uncovers maize regulatory architecture

Only a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified. We developed a computational framework to exploit this sequence arrangement. The models learn to classify regulatory regions based on sequence features - k-mers. To do this, we borrowed two approaches from the field of natural language processing: (1) \"bag-of-words\" which is commonly used for differentially weighting key words in tasks like sentiment analyses, and (2) a vector-space model using word2vec (vector-k-mers), that captures semantic and linguistic relationships between words. We built \"bag-of-k-mers\" and \"vector-k-mers\" models that distinguish between regulatory and non-regulatory regions with an accuracy above 90%. Our \"bag-of-k-mers\" achieved higher overall accuracy, while the \"vector-k-mers\" models were more useful in highlighting key groups of sequences within the regulatory regions. These models now provide powerful tools to annotate regulatory regions in other maize lines beyond the reference, at low cost and with high accuracy.

plant biology↗