About

Andrew Leakey is the Michael Aiken Chair and Professor of Plant Biology at the University of Illinois, with additional professorships in Crop Sciences, the Center for Digital Agriculture, the National Center for Supercomputing Applications (NCSA), and the Carl R. Woese Institute for Genomic Biology. He also serves as the Director of the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) within the Office of the Vice Chancellor for Research and Innovation. His academic background includes a B.Sc. (1998) and Ph.D. (2003) from the University of Sheffield, a Fulbright Scholarship at UIUC (2002-2003), and postdoctoral and research fellow positions at the University of Illinois and the Institute for Genomic Biology, respectively. Professor Leakey's research focuses on integrative plant physiology, genetics, and genomics, particularly addressing plant water use efficiency, photosynthesis, and carbon metabolism. His work investigates crop responses to elevated CO2, drought, temperature, and ozone, with an emphasis on crop sustainability and adaptation to global environmental change. His group employs a vertically integrated phenotyping approach combining genetic, molecular, biochemical, physiological, and ecological tools to assess plant performance under both field and controlled conditions. A major focus is understanding the genetic and physiological controls of stomatal patterning and photosynthetic water use efficiency (WUE), utilizing molecular genetics, quantitative genetics, and physiology. This research has practical applications in biotechnology and breeding aimed at enhancing WUE through manipulation of stomatal patterning. His research advances include high-throughput phenotyping of stomatal traits, development of transgenic germplasm and natural diversity collections, and establishment of large-scale field facilities for drought treatment experiments. Leakey's work contributes to improving mechanistic understanding of plant water relations, photosynthesis, respiration, and plant responses to global environmental change, ultimately aiming to enhance ecosystem services and crop production under changing environmental conditions.

Research topics

• Biology
• Environmental science
• Botany
• Ecology
• Agronomy
• Genetics
• Environmental resource management
• Natural resource economics
• Geography

Selected publications

• Multisite Field Evaluation of Oil Accumulation and Agronomic Performance in Grain and Sweet Sorghums Engineered for Lipid Hyperaccumulation

  Plant Biotechnology Journal · 2026-03-25

  articleOpen access

  ABSTRACT Oil sorghum (OS) has been developed by engineering grain (TX430) and sweet (Ramada) genetic backgrounds to accumulate triacylglycerols (TAG) in vegetative tissues as an energy‐dense feedstock for sustainable aviation fuel (SAF) and other biofuels. This study evaluated two TX430 OS lines (TxHO‐2, TxHO‐3) and two Ramada OS lines (RmHO‐1, RmHO‐2) alongside wild‐type (WT) lines in NE and IL over 2 years (2023–2024) to quantify genotype × environment effects on agronomic performance and TAG accumulation. Across four environments, TX430 OS lines showed average TAG concentrations of 15.0 g kg −1 in leaves and 12.8 g kg −1 in stems, approximately 19‐fold higher than WT. Ramada OS lines accumulated 26.1 g kg −1 in leaves and 12.3 g kg −1 in stems, approximately 25‐fold and 13‐fold increases over WT, respectively. OS lines in TX430 exhibited an 18% reduction in biomass (8.4 vs. 9.9 Mg ha −1 for WT), while Ramada OS lines had similar WT biomass (18.3 vs. 19.9 Mg ha −1 for WT). Among TX430 OS lines, TxHO‐2 achieved the highest TAG yield (190 kg ha −1 ), while RmHO‐1 led the Ramada lines (335 kg ha −1 ) due to higher biomass and similar TAG concentration. Enhanced TAG accumulation increased N, P, and K removal in TX430 lines but not in Ramada lines. Structural carbohydrate and ash concentration were unaffected. Overall, results confirm vegetative lipid accumulation as a viable strategy for high‐biomass sorghum, supporting its potential as a dual‐purpose feedstock for SAF. Future work should focus on minimizing biomass yield penalties and improving nutrient use efficiency in oil sorghum systems.

  PublisherDOI

• Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize

  Illinois Data Bank · 2026-01-01

  datasetOpen accessSenior author

  Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

  PublisherDOI

• Data for "High-resolution microCT reveals relationships between stomata and interior leaf anatomy in Sorghum"

  Open MIND · 2026-01-01

  datasetOpen access1st authorCorresponding

  Includes three different types of stacks, in two folders: "REC_RAW_STACKS.zip" contains: (1) Raw gray-scale reconstructed microCT x-ray scans, in the form of individual stacks per sample. "ML_STACKS.zip" contains: (2) Stacks that have been labeled using a machine-learning mask-RCNN pipeline identifying epidermis, mesophyll, airspace, vascular bundle, and background. (3) Stacks that have stomata locations labeled using a small point. The three types of stacks were used to calculate a variety of anatomical and physiological traits, using ImageJ Macros provided on Github: https://github.com/leakey-lab/microCT-Macros-Fischer. Young leaves from WT and transgenic Sorghum plants. CSV "microCT_REC2_META.csv" contains metadata, including sample/sub-sample labels.

  PublisherDOI

• Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

  Open MIND · 2026-01-01

  dataset

  This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

  DOI

• Data for Brachypodium SPEECHLESS2 promoter drives expression of a synthetic EPF to reduce stomatal density in sugarcane without pleiotropic effects

  Illinois Data Bank · 2026-01-01

  datasetOpen accessSenior author

  Datasheets relating to the article "Brachypodium SPEECHLESS2 promoter drives expression of a synthetic EPF to reduce stomatal density in sugarcane without pleiotropic effects" published in Plant Biotechnology Journal.

  PublisherDOI

• Breaking the barrier of human-annotated training data for machine-learning-aided plant research using aerial imagery.

  2025-04-04

  preprintOpen accessSenior author
  PublisherDOI

• Phylogenetic diversity of light-dependent phosphorylation of Thr78 in Rubisco activase

  Journal of Experimental Botany · 2025-06-17

  articleOpen access

  Rubisco activase is an ATP-dependent chaperone that facilitates dissociation of inhibitory sugar phosphates from the catalytic sites of Rubisco during photosynthesis. In Arabidopsis, Rubisco activase is negatively regulated by dark-dependent phosphorylation of Thr78. The prevalence of Thr78 in Rubisco activase was investigated across sequences from 91 plant species, finding that 29 (∼32%) species shared a threonine in the same position. Analysis of seven C3 species with an antibody raised against a Thr78 phospho-peptide demonstrated that this position is phosphorylated in multiple genera. However, light-dependent dephosphorylation of Thr78 was observed only in Arabidopsis. Further, phosphorylation of Thr78 could not be detected in any of the four C4 grass species examined. The results suggest that despite conservation of Thr78 in Rubisco activase from a wide range of species, a regulatory role for phosphorylation at this site is more limited. This provides a case study for how variation in post-translational regulation can amplify functional divergence across the phylogeny of plants beyond what is explained by sequence variation in a metabolically important protein.

  PublisherOA PDFDOI

• Carbon availability, soil pH, and microbial allocation to nitrogen acquisition shape grassland heterotrophic respiration in response to a decade of nitrogen addition

  Soil Biology and Biochemistry · 2025-10-04

  article
  PublisherDOI

• Constitutive down‐regulation of liguleless alleles in sorghum drives increased productivity and water use efficiency

  Plant Biotechnology Journal · 2025-06-01 · 3 citations

  articleOpen access

  Plant architecture influences the microenvironment throughout the canopy layer. Plants with a more erect leaf architecture allow for an increase in planting densities and allow more light to reach lower canopy leaves. This is predicted to increase crop carbon assimilation. Frictional resistance to wind reduces air movement in the lower canopy, resulting in higher humidity. By increasing the proportion of canopy photosynthesis in the more humid lower canopy, gains in the efficiency of water use might be expected, although this may be slightly offset by the more open erectophile form canopy. An anatomical feature in members of the Poaceae family that impacts leaf angle is the articulated junction of the sheath and blade, which also bares the ligule and auricles. Mutants, which lack ligules and auricles, show no articulation at this junction, resulting in leaves that are near vertical. In maize, these phenotypes termed liguleless result from null mutations of genes: ZmLG1 (Zm00001eb67740) and ZmLG2 (Zm00001eb147220). In sorghum, SbiRTx430.06G264300 (SbLG1) and SbiRTx430.03G392300 (SbLG2) are annotated as the respective maize homologues. A hair-pin element designed to down-regulate both SbLG1 and SbLG2 was introduced into the grain sorghum genotype RTx430. Derived transgenic events harbouring the hair-pin failed to develop ligules and displayed reduced leaf angles to the vertical, but less vertical than in null mutations. Under field settings, plots sown with these sorghum events having an erect architecture phenotype displayed an increase in photosynthesis in lower canopy levels, which led to increases in above-ground biomass and seed yield, without an increase in water use.

  PublisherOA PDFDOI

• GNC is a regulator of metabolic and productivity responses to elevated CO ₂ in <i>Arabidopsis thaliana</i>

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-01

  preprintOpen accessSenior authorCorresponding

  Abstract Despite established understanding of plant physiological responses to elevated [CO 2 ], the underlying genes are poorly understood. Soybean transcriptomics previously identified a GATA transcription factor, involved in carbon and nitrogen metabolism, as responsive to elevated [CO 2 ]. Supported by in silico modeling, we therefore hypothesized that this gene plays a previously unrecognized role in responding to elevated [CO 2 ]. Wildtype and a T-DNA insertion line of Arabidopsis thaliana for GNC (GATA, Nitrate Inducible, Carbon Metabolism Involved) were grown under three treatments: sustained ambient [CO 2 ], sustained elevated [CO 2 ], and transfer from ambient to elevated [CO 2 ], to assess changes in their physiology, biochemistry, and transcriptome. Photosynthetic and biomass responses to elevated [CO 2 ] and transfer [CO 2 ] in plants lacking GNC were significantly weaker than WT. A lag of 25-73 hrs in transcriptomic responses after transfer to elevated [CO 2 ] was consistent with indirect sensing, presumably via sugar signals. The breakdown of the gene expression network around GNC was most pronounced in the transfer treatment and suggests targets for further study of interactions between elevated [CO 2 ] and sulfur and nitrogen metabolism. This work provides a case study of a CO 2 -responsive transcription factor that may be a compelling target for adapting crops to future growing conditions after further characterization. Summary Statement A GATA transcription factor modulates plant metabolic and productivity responses to elevated CO 2 .

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: RoL-Rules for Dynamic-Light Environmental Sculpting of Genomes

  NSF · $1.2M · 2021–2026

Frequent coauthors

• Elizabeth A. Ainsworth

  124 shared

• Stephen P. Long

  74 shared

• Donald R. Ort

  University of Illinois Urbana-Champaign

  69 shared

• Alistair Rogers

  Lawrence Berkeley National Laboratory

  63 shared

• Edward S. Buckler

  Cornell University

  58 shared

• Carl J. Bernacchi

  University of Illinois Urbana-Champaign

  42 shared

• Patrick J. Brown

  33 shared

• Roberto Lozano

  Ginkgo BioWorks (United States)

  30 shared

Labs

• Andrew Leakey's LaboratoryPI

  The Leakey Lab is a collaborative team from across the globe!

Awards & honors

• Fulbright Scholar (2002-2003)
• Beckman Fellow (2011)
• I.C. Gunsalus Fellow (2013)
• Calvin-Benson Award for Early Career Excellence in Photosynt…
• University Scholar (2017)