Biology subjects

Kelley, D. R.

Publications and source records attributed to Kelley, D. R..

At least 19 recordsLinked to original sources

bioRxiv2026

A high-resolution protein-RNA binding atlas for structure-aware interaction prediction and post-transcriptional regulatory modeling

RNA-binding proteins (RBPs) control post-transcriptional gene expression by regulating RNA processing, localization, stability, and translation. However, our understanding of RNA regulation remains limited by the resolution, scale, and uniformity of available RBP binding maps. Here we introduce eclair CLIP (ecCLIP), a high-throughput CLIP-seq platform that combines endogenous protein tagging for uniform affinity capture with ligation-free library preparation to generate high-resolution RNA-protein binding profiles. We applied ecCLIP to 67 endogenously tagged RBPs in HEK293T cells, generating a high-quality transcriptome-scale atlas that recovered known binding preferences and captured post-transcriptional regulons linked to RNA half-life. To test the utility of the atlas, we incorporated ecCLIP into Borzuki, a Borzoi-derived sequence-to-function model trained on complementary RNA degradation, expression, CLIP, and Perturb-seq data. Inclusion of ecCLIP data improved RNA half-life prediction and assessment of 3' UTR variant effects on RNA stability, demonstrating that our atlas helps capture RNA dynamics. To directly model the cis-regulatory grammar underlying these RBP-RNA interactions, we developed Pythia, a structure-aware neural network architecture that incorporates context-free grammar-inspired fixed convolutions to explicitly encode RNA base-pairing features. Pythia predicted RBP binding from sequence and structural context and decomposed predictions into sequence- and structure-dependent components. We tested this decomposition for five RBPs using massively parallel reporter CLIP (MPRC) binding assays in cells, using synthetic RNA libraries containing mutations designed to preserve or disrupt predicted RNA structures across nominated binding sites. We used the same MPRC strategy to confirm allele-specific binding effects for variants prioritized by Pythia. Together, ecCLIP and Pythia provide a resource for structure-aware decoding of RNA-protein interaction grammar and interpretation of post-transcriptional regulatory variation.

genomics↗

bioRxiv2026

Cerberus: bidirectional state space blocks improve accuracy and efficiency of regulatory sequence models

Sequence-to-function models predict regulatory activity and variant effects from DNA sequence alone, and leading models such as Borzoi compute long-range interactions with self-attention. The reference genome caps unique training sequences, and new assays add labels, not sequences. This constraint favors architectures that learn more from limited sequence diversity. We compared long-range blocks with the rest of the architecture and the training data held fixed, replacing Borzoi's transformer blocks with Hydra, a bidirectional state space block built on Mamba-2. Hydra blocks ran 5.7-fold faster on 8,192-token inputs, and models built on them predicted binned coverage on held-out sequences and classified fine-mapped eQTLs more accurately. Hydra's linear cost in sequence length let us run the blocks at 32 bp resolution and remove the U-net decoder, leaving a convolution-SSM model smaller and more accurate than the one it replaced. Scaling this architecture up on an augmented track collection produced Cerberus, an ensemble of eight models over 786 kb of input. We evaluated it on GTEx v11 fine-mapped eQTL, sQTL, and paQTL benchmarks with negatives matched on allele frequency, gene expression, and phenotype-specific positional annotations to reduce confounding by these properties. Cerberus is more accurate than Borzoi on all three phenotypes, 0.692 versus 0.668 mean eQTL AUPRC across 48 tissues, and estimates eQTL effect sizes better, 0.379 versus 0.321 Spearman $\rho$. Against AlphaGenome, it trades leads in eQTL classification, ahead on variants 3 to 100 kb from the transcription start site and behind in coding sequence and within 3 kb. Interpretation of the trained blocks shows interaction range growing with block depth, from a 0.9 kb median half-life in the first block to 79 kb in the seventh, and the deepest heads anchoring on promoters, enhancers, and CTCF peaks. We release the models, the training data, the training and evaluation code, and the benchmark sets, supporting variant scoring and transfer learning.

genomics↗

bioRxiv2026

Scalable saturation mutagenesis reveals gene regulatory architecture and rare variant effects

Long-context sequence-to-function models enable nucleotide-resolution prediction of regulatory variant effects, motivating comprehensive mutational interrogation across broad genomic contexts. Yet conventional in silico saturation mutagenesis (ISM) scores mutations one at a time, requiring millions of model evaluations for a single gene and billions to trillions at genome scales. Here, we introduce Multi-ISM, a scalable framework that reformulates ISM as a sparse recovery problem. Multi-ISM generates mutational maps using 45-fold fewer model evaluations than exhaustive single-variant ISM and matches or exceeds its accuracy on variant-effect benchmarks. Multi-ISM is architecture-agnostic and transfers across large-scale sequence-to-function models. We applied Multi-ISM to 5,000 protein-coding genes, including 3,317 OMIM disease genes, generating base-pair-resolution, tissue-resolved attribution maps across 500-kb windows. These maps supported enhancer--gene prioritization and identification of cell-type-specific regulatory elements. Gene-level summaries of the maps captured regulatory complexity and showed that more constrained genes had smaller predicted mutational effects. Aggregating Multi-ISM predictions into gene-level rare-variant burdens improved personalized expression prediction over a common-variant elastic net, with the largest gains at expression outliers. Multi-ISM makes nucleotide-resolution interpretation of long-context sequence models a routine computation rather than a dedicated effort, so that new architectures, functional readouts, and cellular contexts can be mapped as they appear.

genomics↗

bioRxiv2026

Sequence design for three-dimensional genome folding using Akita Semifreddo

Mammalian genomes display complex three-dimensional organization which is crucial for downstream processes like gene regulation. Local features of genome organization are largely driven by loop extrusion and manifest as boundaries, dots, and flames in genome contact maps. Still, the rational design of DNA sequences that produce desired folding patterns has not been demonstrated. Here, we present Akita Semifreddo, a framework that enables the rational in silico design of DNA sequences with programmable 3D folding outcomes. This combines a computationally efficient "half-frozen" version of the AkitaV2 genome folding model with the Ledidi sequence optimizer. We systematically demonstrate that this approach spans the full repertoire of known local folding features. We show that [~]2 kb synthetic sequences can be designed to induce boundaries, dots, and flames at desired strengths, with CTCF motif configurations consistent with their known mechanistic bases. We further demonstrate that weak boundaries can be designed through transcription-associated sequence features alone, without introducing CTCF motifs, and that strong boundaries can be suppressed by introducing SINE B2 retroelement-like sequences. Collectively, these results reveal a many-to-one relationship between DNA sequence and folding outcomes and uncover the biological basis of sequence features leveraged by our model. In short, Akita Semifreddo provides a platform for dissecting the sequence grammar of three-dimensional chromatin architecture and engineering synthetic regulatory landscapes.

genomics↗

bioRxiv2025

Predicting dynamic expression patterns in budding yeast with a fungal DNA language model

Predicting gene expression from DNA sequence remains challenging due to complex regulatory codes. We introduce a masked DNA language model pretrained on 165 fungal genomes closely related to budding yeast that captures conserved regulatory grammar. Fine-tuning the LM on yeast RNA-seq data--including high-resolution transcriptional regulator induction time courses generated in this study--yielded Shorkie, a model that substantially improves gene expression prediction compared to baselines trained without self-supervision. Shorkie identified canonical transcription factor (TF) binding motifs and tracked their usage across induction experiments. Furthermore, Shorkie accurately predicted variant effects, outperforming leading sequence-to-expression models in cis-eQTL classification and achieving high concordance with massively parallel reporter assays. Interpretability analyses revealed Shorkies ability to resolve promoter dynamics, splicing signals, and temporal changes in regulatory motif usage. This framework demonstrates that evolutionary-scale pretraining combined with transfer learning substantially improves our ability to decode gene regulation from sequence, providing insights into noncoding variants and regulatory networks.

genomics↗

bioRxiv2025

Borzoi-informed fine mapping improves causal variant prioritization in complex trait GWAS

1Genome-wide association studies (GWAS) have identified thousands of trait-associated loci. Prioritizing causal variants within these loci is critical for characterizing trait biology. Statistical fine mapping identifies causal variants at trait-associated loci, but linkage disequilibrium (LD) and limited GWAS sample sizes prevent the resolution of many associations. Functionally informed approaches augment fine mapping by estimating variant prior causal probabilities based on overlap with trait-relevant functional annotations. However, functional enrichment provides only an indirect proxy for variant functional impact. Sequence-to-function models directly estimate variant effects on molecular phenotypes from underlying sequence context. Borzoi is a long-context model that predicts sequence determinants of transcription, splicing, and polyadenylation across diverse tissues and cell types. Here we present Sniff, a Borzoi-informed fine-mapping approach that integrates broad genomic functional annotations with Borzoi-predicted variant effects via PolyFun to estimate variant prior causal probabilities. Applied to 15 UK Biobank traits, Sniff identifies 9.45% additional fine-mapped variants compared to PolyFun-Baseline at posterior inclusion probability (PIP) > 0.8. Sniff-prioritized variants exhibit allele-specific activity in reporter assays and are predicted to have tissue-specific activity in trait-relevant tissues. For most traits, genes nominated by Sniff receive higher scores from the orthogonal gene prioritization method PoPS compared to genes nominated using functional annotations alone. Because differentially prioritized variants are driven by Borzoi predictions, we leverage attribution techniques to characterize sequence features underlying fine mapping and generate mechanistic hypotheses for GWAS associations.

genetics↗

bioRxiv2025

Predicting cell type-specific coverage profiles from DNA sequence

Predicting expression profiles from RNA-seq experiments provides a powerful approach for universal sequence-based variant effect prediction, enabling researchers to score variants that affect total gene expression and relative isoform abundances. These models can be repurposed for new prediction tasks through transfer learning. However, current base models train primarily on bulk RNA-seq profiles derived from tissues and cell lines, overlooking the wealth of single-cell 3-seq data that captures cell type-specific gene regulation. Here, we extend the capabilities of our recently developed Borzoi model by training on single-cell 3-seq expression profiles from the Tabula Sapiens, Tabula Muris, and the Adult Brain Atlas, aggregated by cell type. This new model, Borzoi Prime, enables accurate variant interpretation across diverse cells, spanning erythrocytes to microglia. Training on 3-seq profiles improves the models ability to predict cell- and tissue-specific alternative polyadenylation, even in the original bulk RNA-seq data. Through UTR-wide mutagenesis experiments of alternatively polyadenylated genes, we highlight determinants of cell type-specific 3 UTR regulation learned by the model. This cell type-resolved approach opens new possibilities for understanding genetic variant effects via multiple layers of regulation in specific cellular contexts.

genomics↗

bioRxiv2025

Parameter-Efficient Fine-Tuning of a Supervised Regulatory Sequence Model

1DNA sequence deep learning models accurately predict epigenetic and transcriptional profiles, enabling analysis of gene regulation and genetic variant effects. While large-scale training models like Enformer and Borzoi are trained on abundant data, they cannot cover all cell states and assays, necessitating new model training to analyze gene regulation in novel contexts. However, training models from scratch for new datasets is computationally expensive. In this study, we systematically evaluate a transfer learning framework based on parameter-efficient fine-tuning (PEFT) approaches for supervised regulatory sequence models. We focus on the recently published state-of-the-art model Borzoi and fine-tune to new RNA-seq datasets, enabling accurate and efficient analysis of gene regulation across different biological systems. Our results demonstrate that PEFT substantially improves memory and runtime efficiency while achieving high accuracy. The transferred models effectively predict held-out gene expression changes, identify regulatory drivers of differentially expressed genes, and predict cell-type-specific variant effects. Our findings underscore the potential of PEFT to enhance the utility of large-scale sequence models like Borzoi for broader application to study genetic variants and regulatory drivers in any functional genomics dataset. Code for efficient Borzoi transfer is available in the Baskerville codebase https://github.com/calico/baskerville.

bioinformatics↗

bioRxiv2025

Shift augmentation improves DNA convolutional neural network indel effect predictions

Determining genetic variant effects on molecular phenotypes like gene expression is a task of paramount importance to medical genetics. DNA convolutional neural networks (CNNs) attain state-of-the-art performance at predicting variant effects on gene regulation. However, most applications of such models focus on single nucleotide polymorphisms (SNPs), as technical challenges limit their application to insertions and deletions (indels). Sequence shifts from indels introduce technical variance in deep CNNs through misalignment of pooling blocks and output boundaries, creating artificially inflated variant effect scores compared to SNPs and confounding their interpretation. In this work, we demonstrate this technical variance in model predictions and present two strategies based on data augmentation with sequence shifts that reduce it. Applied to the state-of-the-art Borzoi model, our stitching approach improves indel eQTL classification accuracy across GTEx tissues. Furthermore, we demonstrate these techniques and observe compelling eQTL concordance for larger structural variants and tandem repeats. We additionally introduce in silico deletion (ISD) as an interpretation technique and validate it using MPRA data, demonstrating concordance between predicted and experimental measurements for deletion effects. Our strategies expand the utility of regulatory sequence machine learning for studying the full spectrum of noncoding genetic variation in human development and disease.

genomics↗

bioRxiv2025

Selective State Space Models Outperform Transformers at Predicting RNA-Seq Read Coverage

Transformers are the basis for many state-of-the-art machine learning tools, including those for predicting gene expression data from DNA sequence. The considerable time and cost of training transformer models has motivated development of alternative approaches inspired by ideas from the signal-processing literature, such as state-space models (Mamba), Fourier transforms (Hyena), and wavelet transforms (MultiResNet). To evaluate these methods as potential replacements (or complements) for attention, we developed a software library bilby, implemented using Python and Jax/Flax, providing convolutional, attention, bidirectional Hyena, bidirectional Mamba, and striped-architecture models for supervised multi-task learning in functional genomics. We report a comparison of these architectures, testing several hyperparameters and variations, and reporting performance statistics for the withheld test set as well as downstream SNP classifiers. Relative to models comprising convolution and attention layers (implemented in Python and TensorFlow via the Baskerville library used by the Borzoi software), models comprising convolutional, bidirectional Mamba, and (optionally) attention layers achieve small but consistent improvements in prediction accuracy, for roughly comparable training times and parameter counts, when averaged across all output tracks and data splits (a proportional increase of 3-4% in Pearson R, and 1-2% in r2, with the highest gains achieved when Mamba and attention layers were combined in a striped architecture). In contrast, Hyena (when reimplemented as described in the literature) was not competitive with attention-based models at these tasks, while MultiResNet proved too slow to be practical. The gains in prediction accuracy of the Mamba-based models do not yet translate to significantly improved performance on downstream SNP classification tasks: benchmarks using a GTEx eQTL dataset yield roughly similar results for Mamba- and attention-based classifiers, with attention marginally outperforming Mamba in one metric (a difference of +0.007 in area under ROC) and slightly underperforming by another metric (a difference of -0.006 in Spearman rank correlation). We argue that these results suggest selective state-space models (such as Mamba and Striped Mamba) warrant further exploration for functional genomics tasks. Our code and trained models are publicly available at https://github.com/ihh/bilby.

genomics↗

bioRxiv2024

Identification of phenotypic and transcriptomic signatures underpinning maize crown root systems

Maize is pivotal in supporting global agriculture and addressing food security challenges. Crop root systems are critical for water uptake and nutrient acquisition, which impacts yield. Quantitative trait phenotyping is essential to understand better the genetic factors underpinning maize root growth and development. Root systems are challenging to phenotype given their below-ground, soil-bound nature. In addition, manual trait annotations of root images are tedious and can lead to inaccuracies and inconsistencies between individuals, resulting in data discrepancies. To address these issues, we have developed an automated phenotyping pipeline for field-grown maize crown roots by leveraging open-source software. Phenotypic variation of 20 maize genotypes from the Wisconsin Diversity panel was significant for numerous root traits, suggesting a genetic basis for the observed developmental deviations. In addition, juvenile root traits from controlled environment conditions exhibited inconsistent correlation with field-grown adult root traits, underscoring the developmental plasticity prevalent during maize root morphogenesis. Transcripts involved in hormone signaling and stress responses were among differentially expressed genes in roots from 20 maize genotypes, suggesting many molecular processes may underlie the observed phenotypic variance. This study furthers our understanding of genotype-phenotype relationships, which is relevant for informing agricultural strategies to improve maize root physiology.

plant biology↗

bioRxiv2024

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2

Interphase mammalian genomes are folded in 3D with complex locus-specific patterns that impact gene regulation. CTCF (CCCTC-binding factor) is a key architectural protein that binds specific DNA sites, halts cohesin-mediated loop extrusion, and enables long-range chromatin interactions. There are hundreds of thousands of annotated CTCF-binding sites in mammalian genomes; disruptions of some result in distinct phenotypes, while others have no visible effect. Despite their importance, the determinants of which CTCF sites are necessary for genome folding and gene regulation remain unclear. Here, we update and utilize Akita, a convolutional neural network model, to extract the sequence preferences and grammar of CTCF contributing to genome folding. Our analyses of individual CTCF sites reveal four predictions: (i) only a small fraction of genomic sites are impactful, (ii) insulation strength is highly dependent on sequences flanking the core CTCF binding motif, (iii) core and flanking sequences are broadly compatible, and (iv) core and flanking nucleotides contribute largely additively to overall strength. Our analysis of collections of CTCF sites make two predictions for multi-motif grammar: (i) insulation strength depends on the number of CTCF sites within a cluster, and (ii) pattern formation is governed by the orientation and spacing of these sites, rather than any inherent specialization of the CTCF motifs themselves. In sum, we present a framework for using neural network models to probe the sequences instructing genome folding and provide a number of predictions to guide future experimental inquiries. Author SummaryMammalian genomes are spatially organized in 3D with profound consequences for all processes involving DNA. CTCF is a key genome organizer, recognizing numerous sites and creating a variety of contact patterns across the genome. Despite the importance of CTCF, the sequence determinants and grammar of how individual sites collectively instruct genome folding remain unclear. This work leverages the ability of Akita, a deep neural network, to make high-throughput predictions for genome folding after DNA sequence perturbations. Using Akita, we make several experimentally testable predictions. First, only a minority of annotated sites individually impact folding, and flanking DNA sequences greatly modulate their impact. Second, multiple sites together influence folding based on their number, orientation, and spacing. In sum, we provide a roadmap for interpreting neural networks to better understand genome folding and important considerations for the design of experiments.

genomics↗

bioRxiv2023

Rewriting regulatory DNA to dissect and reprogram gene expression

Regulatory DNA sequences within enhancers and promoters bind transcription factors to encode cell type-specific patterns of gene expression. However, the regulatory effects and programmability of such DNA sequences remain difficult to map or predict because we have lacked scalable methods to precisely edit regulatory DNA and quantify the effects in an endogenous genomic context. Here we present an approach to measure the quantitative effects of hundreds of designed DNA sequence variants on gene expression, by combining pooled CRISPR prime editing with RNA fluorescence in situ hybridization and cell sorting (Variant-FlowFISH). We apply this method to mutagenize and rewrite regulatory DNA sequences in an enhancer and the promoter of PPIF in two immune cell lines. Of 672 variant-cell type pairs, we identify 497 that affect PPIF expression. These variants appear to act through a variety of mechanisms including disruption or optimization of existing transcription factor binding sites, as well as creation of de novo sites. Disrupting a single endogenous transcription factor binding site often led to large changes in expression (up to -40% in the enhancer, and -50% in the promoter). The same variant often had different effects across cell types and states, demonstrating a highly tunable regulatory landscape. We use these data to benchmark performance of sequence-based predictive models of gene regulation, and find that certain types of variants are not accurately predicted by existing models. Finally, we computationally design 185 small sequence variants ([≤]10 bp) and optimize them for specific effects on expression in silico. 84% of these rationally designed edits showed the intended direction of effect, and some had dramatic effects on expression (-100% to +202%). Variant-FlowFISH thus provides a powerful tool to map the effects of variants and transcription factor binding sites on gene expression, test and improve computational models of gene regulation, and reprogram regulatory DNA.

genetics↗

bioRxiv2023

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancer-promoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

genomics↗

bioRxiv2023

Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation

Sequence-based machine learning models trained on genome-scale biochemical assays improve our ability to interpret genetic variants by providing functional predictions describing their impact on the cis-regulatory code. Here, we introduce a new model, Borzoi, which learns to predict cell- and tissue-specific RNA-seq coverage from DNA sequence. Using statistics derived from Borzois predicted coverage, we isolate and accurately score variant effects across multiple layers of regulation, including transcription, splicing, and polyadenylation. Evaluated on QTLs, Borzoi is competitive with, and often outperforms, state-of-the-art models trained on individual regulatory functions. By applying attribution methods to the derived statistics, we extract cis-regulatory patterns driving RNA expression and post-transcriptional regulation in normal tissues. The wide availability of RNA-seq data across species, conditions, and assays profiling specific aspects of regulation emphasizes the potential of this approach to decipher the mapping from DNA sequence to regulatory function.

genomics↗

bioRxiv2023

The Auxin Response Factor ARF27 is required for maize root morphogenesis

Crop root systems are central to nutrition acquisition and water usage. Root hairs and lateral roots contribute to fine-scale patterning of root systems and can offer many advantages for improving root function without drastically impacting overall system architecture. Key genetic regulators underpinning root hair morphogenesis have been well characterized in the model plant Arabidopsis but are less understood in maize. Here, we identify a novel determinant of root hair morphogenesis and auxin responses in maize, AUXIN RESPONSE FACTOR27 (ARF27), using both reverse and quantitative genetic approaches. ARF27 is enriched in maize primary root tissues at both the transcript and protein level. Loss of ARF27 leads to short primary roots and reduced root hair formation, while lateral root density is unaltered. In arf27 roots, auxin-responsive gene expression is dysregulated, which is consistent with the predicted function of this transcription factor. Moreover, a genome wide association study (GWAS) to uncover genetic determinants of auxin-dependent root traits identified ARF27 as a candidate gene. Furthermore, auxin hypersensitive maize genotypes exhibit altered crown root length and surface area in field-grown plants. A gene regulatory network (GRN) was reconstructed and an ARF27 subnetwork was integrated with DAP-seq and GWAS data to identify ARF27 target genes. The ARF27 GRN includes known maize root development genes, such as ROOTLESS CONCERNING CROWN ROOTS (RTCS), ROOTHAIRLESS 3 (RTH3) and RTH6. Altogether this work identifies a novel genetic driver of auxin-mediated root morphogenesis in maize that can inform agricultural strategies for improved crop performance.

plant biology↗

bioRxiv2023

ZmPILS6 is an auxin efflux carrier required for maize root morphogenesis

Plant root systems play a pivotal role in plant physiology and exhibit diverse phenotypic traits. Understanding the genetic mechanisms governing root growth and development in model plants like maize is crucial for enhancing crop resilience to drought and nutrient limitations. This study focused on identifying and characterizing ZmPILS6, an annotated auxin efflux carrier, as a key regulator of various crown root traits in maize. ZmPILS6-modified roots displayed reduced network area and suppressed lateral root formation, desirable traits during drought and low phosphate conditions. The research revealed that ZmPILS6 localizes to the endoplasmic reticulum and plays a vital role in controlling the spatial distribution of indole-3-acetic acid (IAA or "auxin") in primary roots. The study also demonstrated that ZmPILS6 can actively efflux IAA when expressed in yeast. Furthermore, the loss of ZmPILS6 resulted in significant proteome remodeling in maize roots, particularly affecting hormone signaling pathways. To identify potential interacting partners of ZmPILS6, a weighted gene co-expression analysis (WGNA) was performed. Altogether, this research contributes to the growing knowledge of essential genetic determinants governing maize root morphogenesis, which is crucial for guiding agricultural improvement strategies. Significance StatementCrop yield and stress resilience are significantly influenced by crown root architecture. A reverse genetic screen aimed at identifying novel regulators of maize root morphogenesis led to the discovery of ZmPILS6, an auxin efflux carrier. The loss of ZmPILS6 negatively impacts numerous root traits that are linked to plant physiology and function. Proteomic characterization of pils6-1 roots revealed that this evolutionarily conserved transporter is required for the proper expression of numerous phytohormone pathways, including abscisic acid, gibberellins, and jasmonic acid. Notably, ZmPILS6 appears to have a contrasting role in regulating root morphogenesis compared to its Arabidopsis ortholog, PILS6. This finding emphasizes the need for functional characterization of candidate genes directly within key crops of interest, which cannot always be correctly inferred from other model plants.

plant biology↗

bioRxiv2023

GAUT10 is required for Arabidopsis root cell differentiation and elongation

O_LICell wall properties of the root apical meristem (RAM) are poorly understood compared to the elongation and maturation zones of the developing root. GAUT10 is a pectin biosynthesizing enzyme that is post-transcriptionally regulated by auxin that influences Arabidopsis RAM size in a sucrose-dependent manner. C_LIO_LIUsing live-cell microscopy, we have determined the short root phenotype of the gaut10 loss of function allele is due to a reduction in both RAM cell number and epidermal cell elongation. In addition, the absence of GAUT10 leads to a reduction in lateral root cap and epidermal cell marker line expression, indicating root cell differentiation defects in this mutant. C_LIO_LIGAUT10 is required for normal pectin and hemicellulose composition in primary Arabidopsis roots. Loss of GAUT10 leads to a reduction in galacturonic acid and xylose in primary cell walls and alters the presence of rhamnogalacturonan (RG) I and homogalacturonan (HG) polymers in the root. C_LIO_LIAuxin mediated gene expression and metabolism is altered in gaut10 roots, suggesting that cell wall composition may influence auxin pathways. C_LI

plant biology↗