Maize smart-canopy architecture enhances yield at high densities.

Increasing planting density is a key strategy for enhancing maize yields1-3. An ideotype for dense planting requires a 'smart canopy' with leaf angles at different canopy layers differentially optimized to maximize light interception and photosynthesis4-6, among other features. Here we identified leaf angle architecture of smart canopy 1 (lac1), a natural mutant with upright upper leaves, less erect middle leaves and relatively flat lower leaves. lac1 has improved photosynthetic capacity and attenuated responses to shade under dense planting. lac1 encodes a brassinosteroid C-22 hydroxylase that predominantly regulates upper leaf angle. Phytochrome A photoreceptors accumulate in shade and interact with the transcription factor RAVL1 to promote its degradation via the 26S proteasome, thereby inhibiting activation of lac1 by RAVL1 and decreasing brassinosteroid levels. This ultimately decreases upper leaf angle in dense fields. Large-scale field trials demonstrate that lac1 boosts maize yields under high planting densities. To quickly introduce lac1 into breeding germplasm, we transformed a haploid inducer and recovered homozygous lac1 edits from 20 diverse inbred lines. The tested doubled haploids uniformly acquired smart-canopy-like plant architecture. We provide an important target and an accelerated strategy for developing high-density-tolerant cultivars, with lac1 serving as a genetic chassis for further engineering of a smart canopy in maize.

Genetic analysis and QTL mapping for pericarp thickness in maize (Zea mays L.).

Proper pericarp thickness protects the maize kernel against pests and diseases, moreover, thinner pericarp improves the eating quality in fresh corn. In this study, we aimed to investigate the dynamic changes in maize pericarp during kernel development and identified the major quantitative trait loci (QTLs) for maize pericarp thickness. It was observed that maize pericarp thickness first increased and then decreased. During the growth and formation stages, the pericarp thickness gradually increased and reached the maximum, after which it gradually decreased and reached the minimum during maturity. To identify the QTLs for pericarp thickness, a BC4F4 population was constructed using maize inbred lines B73 (recurrent parent with thick pericarp) and Baimaya (donor parent with thin pericarp). In addition, a high-density genetic map was constructed using maize 10 K SNP microarray. A total of 17 QTLs related to pericarp thickness were identified in combination with the phenotypic data. The results revealed that the heritability of the thickness of upper germinal side of pericarp (UG) was 0.63. The major QTL controlling UG was qPT1-1, which was located on chromosome 1 (212,215,145-212,948,882). The heritability of the thickness of upper abgerminal side of pericarp (UA) was 0.70. The major QTL controlling UA was qPT2-1, which was located on chromosome 2 (2,550,197-14,732,993). In addition, a combination of functional annotation, DNA sequencing analysis and quantitative real-time PCR (qPCR) screened two candidate genes, Zm00001d001964 and Zm00001d002283, that could potentially control maize pericarp thickness. This study provides valuable insights into the improvement of maize pericarp thickness during breeding.

Genetic analyses of embryo homology and ontogeny in the model grass Zea mays subsp. mays.

The homology of the single cotyledon of grasses and the ontogeny of the scutellum and coleoptile as the initial, highly modified structures of the grass embryo are investigated using leaf developmental genetics and targeted transcript analyses in the model grass Zea mays subsp. mays. Transcripts of leaf developmental genes are identified in both the initiating scutellum and the coleoptile, while mutations disrupting mediolateral leaf development also disrupt scutellum and coleoptile morphology, suggesting that these grass-specific organs are modified leaves. Higher-order mutations in WUSCHEL-LIKE HOMEOBOX3 (WOX3) genes, involved in mediolateral patterning of plant lateral organs, inform a model for the fusion of coleoptilar margins during maize embryo development. Genetic, RNA-targeting, and morphological evidence supports models for cotyledon evolution where the scutellum and coleoptile, respectively, comprise the distal and proximal domains of the highly modified, single grass cotyledon.

The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture.

Roots anchor plants in soil, and the failure of anchorage (i.e. root lodging) is a major cause of crop yield loss. Anchorage is often assumed to be driven by root system architecture (RSA). We made use of a natural experiment to measure the overlap between the genetic regulation of RSA and anchorage. After one of the most devastating derechos ever recorded in August 2020, we phenotyped root lodging in a maize (Zea mays) diversity panel consisting of 369 genotypes grown in 6 environments affected by the derecho. Genome-wide and transcriptome-wide association studies identified 118 candidate genes associated with root lodging. Thirty-four percent (40/118) of these were homologs of genes from Arabidopsis (Arabidopsis thaliana) that affect traits such as root morphology and lignin content, expected to affect root lodging. Finally, gene ontology enrichment analysis of the candidate genes and their predicted interaction partners at the transcriptional and translational levels revealed the complex regulatory networks of physiological and biochemical pathways underlying root lodging in maize. Limited overlap between genes associated with lodging resistance and RSA in this diversity panel suggests that anchorage depends in part on factors other than the gross characteristics of RSA.

The pore-rhizosheath shapes maize root architecture by enhancing root distribution in macropores.

Pores and old root-channels are preferentially used by roots to allow them to penetrate hard soils. However, there are few studies that have accounted for the effects of pore-rhizosheath on root growth. In this study, we developed an approach by adding the synthetic root exudates using a porous stainless tube with 0.1-mm micropores through a peristaltic pump to reproduce the rhizosheath around the artificial pore, and investigated the effects of pores with and without rhizosheaths on maize root growth in a dense soil. The results indicated that the artificial rhizosheath was about 2.69 mm wide in the region surrounding the pores. The rhizosheath had a higher content of organic carbon, total nitrogen, and abundance of Actinobacteria than that of the bulk soil. Compared with the artificial macropores, the artificial root-pores with a rhizosheath increased the opportunities for root utilisation of the pores space, promoting steeper and deeper root growth. It is concluded that the pore-rhizosheath has a significant impact on root architecture by enhancing root distribution in macropores.

Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping.

The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited "open" versus. "closed" branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.

Phenotype identification and genome-wide association study of ear-internode vascular bundles in maize (Zea mays).

The vascular bundle in the ear-internode of maize is a key conduit for transporting photosynthetic materials between "source" and "sink", making it critically important to examine its micro-phenotypes and genetic architecture to identify advantageous characteristics and cultivate high-yielding and high-quality varieties. Unfortunately, the limited observation methods and scope of study precludes any comprehensive and systematic investigations into the microscopic phenotypes and genetic mechanisms of vascular bundle in maize ear-internode. In this study, 47 phenotypic traits were extracted in 495 maize inbred lines using micro computed tomography (Micro-CT) scanning technology and a deep learning-based phenotype acquisition method for stem vascular bundle, which included stem slice-related, epidermis zone-related, periphery zone-related, inner zone-related and vascular bundles-related traits. Phenotypic analysis indicated that there was extensive phenotypic variation of vascular bundle traits in ear-internode, especially that in the inner zone. Of these, 30 phenotypic traits with heritability greater than 0.70 were conducted for GWAS, and a total of 4,225 significant SNPs and 416 candidate genes with detailed functional annotations were identified. Furthermore, 20 genes were highly expressed in stem-related tissues, especially in maize internodes. Functional analysis of candidate genes indicated that the pathways obtained for candidate genes of different trait groups were distinct, mainly involved in vitamin synthesis and metabolism, transport of substances, carbohydrate derivative catabolic process, protein transport and localization, and anatomical structure development. The results of this study will help to further understand the phenotypic traits of stem vascular bundles and provide a reference for revealing the genetic mechanism of maize ear-internode vascular bundles.

Multiseriate cortical sclerenchyma enhance root penetration in compacted soils.

Mechanical impedance limits soil exploration and resource capture by plant roots. We examine the role of root anatomy in regulating plant adaptation to mechanical impedance and identify a root anatomical phene in maize (Zea mays) and wheat (Triticum aestivum) associated with penetration of hard soil: Multiseriate cortical sclerenchyma (MCS). We characterize this trait and evaluate the utility of MCS for root penetration in compacted soils. Roots with MCS had a greater cell wall-to-lumen ratio and a distinct UV emission spectrum in outer cortical cells. Genome-wide association mapping revealed that MCS is heritable and genetically controlled. We identified a candidate gene associated with MCS. Across all root classes and nodal positions, maize genotypes with MCS had 13% greater root lignin concentration compared to genotypes without MCS. Genotypes without MCS formed MCS upon exogenous ethylene exposure. Genotypes with MCS had greater lignin concentration and bending strength at the root tip. In controlled environments, MCS in maize and wheat was associated improved root tensile strength and increased penetration ability in compacted soils. Maize genotypes with MCS had root systems with 22% greater depth and 49% greater shoot biomass in compacted soils in the field compared to lines without MCS. Of the lines we assessed, MCS was present in 30 to 50% of modern maize, wheat, and barley cultivars but was absent in teosinte and wild and landrace accessions of wheat and barley. MCS merits investigation as a trait for improving plant performance in maize, wheat, and other grasses under edaphic stress.

Research on Corn Leaf and Stalk Recognition and Ranging Technology Based on LiDAR and Camera Fusion.

Corn, as one of the three major grain crops in China, plays a crucial role in ensuring national food security through its yield and quality. With the advancement of agricultural intelligence, agricultural robot technology has gained significant attention. High-precision navigation is the basis for realizing various operations of agricultural robots in corn fields and is closely related to the quality of operations. Corn leaf and stalk recognition and ranging are the prerequisites for achieving high-precision navigation and have attracted much attention. This paper proposes a corn leaf and stalk recognition and ranging algorithm based on multi-sensor fusion. First, YOLOv8 is used to identify corn leaves and stalks. Considering the large differences in leaf morphology and the large changes in field illumination that lead to discontinuous identification, an equidistant expansion polygon algorithm is proposed to post-process the leaves, thereby increasing the average recognition completeness of the leaves to 86.4%. Secondly, after eliminating redundant point clouds, the IMU data are used to calculate the confidence of the LiDAR and depth camera ranging point clouds, and point cloud fusion is performed based on this to achieve high-precision ranging of corn leaves. The average ranging error is 2.9 cm, which is lower than the measurement error of a single sensor. Finally, the stalk point cloud is processed and clustered using the FILL-DBSCAN algorithm to identify and measure the distance of the same corn stalk. The algorithm combines recognition accuracy and ranging accuracy to meet the needs of robot navigation or phenotypic measurement in corn fields, ensuring the stable and efficient operation of the robot in the corn field.

Breeding maize of ideal plant architecture for high-density planting tolerance through modulating shade avoidance response and beyond.

Maize is a major staple crop widely used as food, animal feed, and raw materials in industrial production. High-density planting is a major factor contributing to the continuous increase of maize yield. However, high planting density usually triggers a shade avoidance response and causes increased plant height and ear height, resulting in lodging and yield loss. Reduced plant height and ear height, more erect leaf angle, reduced tassel branch number, earlier flowering, and strong root system architecture are five key morphological traits required for maize adaption to high-density planting. In this review, we summarize recent advances in deciphering the genetic and molecular mechanisms of maize involved in response to high-density planting. We also discuss some strategies for breeding advanced maize cultivars with superior performance under high-density planting conditions.

Tissue-specific transcriptomics reveal functional differences in floral development.

Flowers are produced by floral meristems, groups of stem cells that give rise to floral organs. In grasses, including the major cereal crops, flowers (florets) are contained in spikelets, which contain one to many florets, depending on the species. Importantly, not all grass florets are developmentally equivalent, and one or more florets are often sterile or abort in each spikelet. Members of the Andropogoneae tribe, including maize (Zea mays), produce spikelets with two florets; the upper and lower florets are usually dimorphic, and the lower floret is greatly reduced compared to the upper floret. In maize ears, early development appears identical in both florets but the lower floret ultimately aborts. To gain insight into the functional differences between florets with different fates, we used laser capture microdissection coupled with RNA-sequencing to globally examine gene expression in upper and lower floral meristems in maize. Differentially expressed genes were involved in hormone regulation, cell wall, sugar, and energy homeostasis. Furthermore, cell wall modifications and sugar accumulation differed between the upper and lower florets. Finally, we identified a boundary domain between upper and lower florets, which we hypothesize is important for floral meristem activity. We propose a model in which growth is suppressed in the lower floret by limiting sugar availability and upregulating genes involved in growth repression. This growth repression module may also regulate floret fertility in other grasses and potentially be modulated to engineer more productive cereal crops.

grasviq: an image analysis framework for automatically quantifying vein number and morphology in grass leaves.

Beyond facilitating transport and providing mechanical support to the leaf, veins have important roles in the performance and productivity of plants and the ecosystem. In recent decades, computational image analysis has accelerated the extraction and quantification of vein traits, benefiting fields of research from agriculture to climatology. However, most of the existing leaf vein image analysis programs have been developed for the reticulate venation found in dicots. Despite the agroeconomic importance of cereal grass crops, like Oryza sativa (rice) and Zea mays (maize), a dedicated image analysis program for the parallel venation found in monocots has yet to be developed. To address the need for an image-based vein phenotyping tool for model and agronomic grass species, we developed the grass vein image quantification (grasviq) framework. Designed specifically for parallel venation, this framework automatically segments and quantifies vein patterns from images of cleared leaf pieces using classical computer vision techniques. Using image data sets from maize inbred lines and auxin biosynthesis and transport mutants in maize, we demonstrate the utility of grasviq for quantifying important vein traits, including vein density, vein width and interveinal distance. Furthermore, we show that the framework can resolve quantitative differences and identify vein patterning defects, which is advantageous for genetic experiments and mutant screens. We report that grasviq can perform high-throughput vein quantification, with precision on a par with that of manual quantification. Therefore, we envision that grasviq will be adopted for vein phenomics in maize and other grass species.

A kelch-repeat superfamily gene, ZmNL4, controls leaf width in maize (Zea mays L.).

Leaf width (LW) is an important component of plant architecture that extensively affects both light capture during photosynthesis and grain yield, particularly under dense planting conditions. However, the genetic and molecular mechanisms regulating LW remain largely elusive in maize (Zea mays L.). In this study, qLW4a, a major quantitative trait locus controlling LW, was identified in a population constructed with maize inbred lines PH6WC, with wide leaves, and Lin387, with narrow leaves. Map-based cloning revealed that ZmNL4, a kelch-repeat superfamily gene, emerged to be the candidate for qLW4a, and a single-base deletion in the conserved SMC_prok_B domain of ZmNL4 in Lin387 caused a frame shift, leading to premature termination. Consistently, the knockout of ZmNL4 by CRISPR/Cas9 editing significantly reduced the LW, which was attributed to a reduction in the cell number instead of cell size, indicating a role of ZmNL4 in regulating cell division. Transcriptomic comparison of ZmNL4 knockout lines with the wild type B73-329 revealed that ZmNL4 might participate in cell wall biogenesis, asymmetric cell division, metabolic processes, transmembrane transport and response to external stimulus, etc. These results provide insights into the genetic and molecular mechanisms of ZmNL4 in controlling LW and could potentially contribute to optimizing plant architecture for maize breeding.

A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels.

High-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two-dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.

Maize plant architecture trait QTL mapping and candidate gene identification based on multiple environments and double populations.

BACKGROUND: The plant architecture traits of maize determine the yield. Plant height, ear position, leaf angle above the primary ear and internode length above the primary ear together determine the canopy structure and photosynthetic efficiency of maize and at the same time affect lodging and disease resistance. A flat and tall plant architecture confers an obvious advantage in the yield of a single plant but is not conducive to dense planting and results in high rates of lodging; thus, it has been gradually eliminated in production. Although using plants that are too compact, short and density tolerant can increase the yield per unit area to a certain extent, the photosynthetic efficiency of such plants is low, ultimately limiting yield increases. Genetic mapping is an effective method for the improvement of plant architecture to identify candidate genes for regulating plant architecture traits. RESULTS: To find the best balance between the yield per plant and the yield per unit area of maize, in this study, the F2:3 pedigree population and a RIL population with the same male parent were used to identify QTL for plant height (PH), ear height (EH), leaf angle and internode length above the primary ear (LAE and ILE) in Changchun and Gongzhuling for 5 consecutive years (2016-2020). A total of 11, 13, 23 and 13 QTL were identified for PH, EH, LAE, and ILE, respectively. A pleiotropic consistent QTL for PH overlapped with that for EH on chromosome 3, with a phenotypic variation explanation rate from 6.809% to 21.96%. In addition, there were major consistent QTL for LAE and ILE, and the maximum phenotypic contribution rates were 24.226% and 30.748%, respectively. Three candidate genes were mined from the three consistent QTL regions and were involved in the gibberellin-activated signal pathway, brassinolide signal transduction pathway and auxin-activated signal pathway, respectively. Analysis of the expression levels of the three genes showed that they were actively expressed during the jointing stage of vigorous maize growth. CONCLUSIONS: In this study, three consistent major QTL related to plant type traits were identified and three candidate genes were screened. These results lay a foundation for the cloning of related functional genes and marker-assisted breeding of related functional genes.

GIF1 controls ear inflorescence architecture and floral development by regulating key genes in hormone biosynthesis and meristem determinacy in maize.

BACKGROUND: Inflorescence architecture and floral development in flowering plants are determined by genetic control of meristem identity, determinacy, and maintenance. The ear inflorescence meristem in maize (Zea mays) initiates short branch meristems called spikelet pair meristems, thus unlike the tassel inflorescence, the ears lack long branches. Maize growth-regulating factor (GRF)-interacting factor1 (GIF1) regulates branching and size of meristems in the tassel inflorescence by binding to Unbranched3. However, the regulatory pathway of gif1 in ear meristems is relatively unknown. RESULT: In this study, we found that loss-of-function gif1 mutants had highly branched ears, and these extra branches repeatedly produce more branches and florets with unfused carpels and an indeterminate floral apex. In addition, GIF1 interacted in vivo with nine GRFs, subunits of the SWI/SNF chromatin-remodeling complex, and hormone biosynthesis-related proteins. Furthermore, key meristem-determinacy gene RAMOSA2 (RA2) and CLAVATA signaling-related gene CLV3/ENDOSPERM SURROUNDING REGION (ESR) 4a (CLE4a) were directly bound and regulated by GIF1 in the ear inflorescence. CONCLUSIONS: Our findings suggest that GIF1 working together with GRFs recruits SWI/SNF chromatin-remodeling ATPases to influence DNA accessibility in the regions that contain genes involved in hormone biosynthesis, meristem identity and determinacy, thus driving the fate of axillary meristems and floral organ primordia in the ear-inflorescence of maize.

Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.

Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but using multimodal data to estimate maize LAI, and the effect of tassels and soil background, remain understudied. Our research aims to (1) determine how multimodal data contribute to LAI and propose a framework for estimating LAI based on remote-sensing data, (2) evaluate the robustness and adaptability of an LAI estimation model that uses multimodal data fusion and deep neural networks (DNNs) in single- and whole growth stages, and (3) explore how soil background and maize tasseling affect LAI estimation. To construct multimodal datasets, our UAV collected red-green-blue, multispectral, and thermal infrared images. We then developed partial least square regression (PLSR), support vector regression, and random forest regression models to estimate LAI. We also developed a deep learning model with three hidden layers. This multimodal data structure accurately estimated maize LAI. The DNN model provided the best estimate (coefficient of determination [R2] = 0.89, relative root mean square error [rRMSE] = 12.92%) for a single growth period, and the PLSR model provided the best estimate (R2 = 0.70, rRMSE = 12.78%) for a whole growth period. Tassels reduced the accuracy of LAI estimation, but the soil background provided additional image feature information, improving accuracy. These results indicate that multimodal data fusion using low-cost UAVs and DNNs can accurately and reliably estimate LAI for crops, which is valuable for high-throughput phenotyping and high-spatial precision farmland management.

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays).

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

Three-Dimensional Time-Lapse Analysis Reveals Multiscale Relationships in Maize Root Systems with Contrasting Architectures.

Understanding how an organism's phenotypic traits are conditioned by genetic and environmental variation is a central goal of biology. Root systems are one of the most important but poorly understood aspects of plants, largely due to the three-dimensional (3D), dynamic, and multiscale phenotyping challenge they pose. A critical gap in our knowledge is how root systems build in complexity from a single primary root to a network of thousands of roots that collectively compete for ephemeral, heterogeneous soil resources. We used time-lapse 3D imaging and mathematical modeling to assess root system architectures (RSAs) of two maize (Zea mays) inbred genotypes and their hybrid as they grew in complexity from a few to many roots. Genetically driven differences in root branching zone size and lateral branching densities along a single root, combined with differences in peak growth rate and the relative allocation of carbon resources to new versus existing roots, manifest as sharply distinct global RSAs over time. The 3D imaging of mature field-grown root crowns showed that several genetic differences in seedling architectures could persist throughout development and across environments. This approach connects individual and system-wide scales of root growth dynamics, which could eventually be used to predict genetic variation for complex RSAs and their functions.

Morphological Characterization and Transcriptome Analysis of New Dwarf and Narrow-Leaf (dnl2) Mutant in Maize.

Lodging is the primary factor limiting high yield under a high plant density. However, an optimal plant height and leaf shape can effectively decrease the lodging risk. Here we studied an ethyl methanesulfonate (EMS)-induced dwarf and a narrow-leaf mutant, dnl2. Gene mapping indicated that the mutant was controlled by a gene located on chromosome nine. Phenotypic and cytological observations revealed that dnl2 showed inhibited cell growth, altered vascular bundle patterning, and disrupted secondary cell wall structure when compared with the wild-type, which could be the direct cause of the dwarf and narrow-leaf phenotype. The phytohormone levels, especially auxin and gibberellin, were significantly decreased in dnl2 compared to the wild-type plants. Transcriptome profiling of the internodes of the dnl2 mutant and wild-type revealed a large number of differentially expressed genes enriched in the cell wall biosynthesis, remodeling, and hormone biosynthesis and signaling pathways. Therefore, we suggest that crosstalk between hormones (the altered vascular bundle and secondary cell wall structure) may contribute to the dwarf and narrow-leaf phenotype by influencing cell growth. These results provide a foundation for DNL2 gene cloning and further elucidation of the molecular mechanism of the regulation of plant height and leaf shape in maize.