Mekkaoui, F. | Search bioRxiv

bioRxiv2026

Modular Scaffold Crystals for Programmable Installation and Structural Observation of DNA-Binding Proteins

Inducing biomacromolecules to self-assemble into diffraction-quality crystals remains a major challenge, typically overcome by brute-force experimental screening. Inspired by Seemans vision of DNA-junction-based scaffolds organizing guest biomacromolecules, we developed a protein-DNA co-crystal combining modular DNA programmability with robust protein-lattice diffraction. Our engineered co-crystals are composed of stacked double-stranded DNA scaffolded by protein columns, surrounding solvent channels designed to enable guest protein diffusion. DNA strut variation allows positionally-controlled installation of diverse DNA binding guest proteins. Experimentally, we simply grow scaffold crystals under standardized conditions, ligate the scaffold, and soak guest proteins. Decoupling crystal growth from guest installation will enable high-throughput structure determination of diverse DNA-binding proteins and protein-macromolecule conjugates. Sub-nanometer position and orientation control of guest macromolecules will also enable functional applications beyond structural biology.

bioengineering↗

bioRxiv2026

Molecular and phenotypic footprints of climate in native Arabidopsis thaliana

Climate change poses a major threat to humanity by driving biodiversity loss and reducing crop yields1,2. To understand the molecular and developmental impacts of rising temperatures, plant science has relied heavily on the model organism Arabidopsis thaliana. Despite decades of research, its development under fully natural conditions remains poorly understood, and only [~]30% of genes have experimental functional annotations, largely because many functions are subtle or manifest only in specific laboratory or ecological contexts3. Here, we address this gap with a landscape transcriptomic approach that integrates intensive phenotyping and transcriptomic profiling of naturally occurring plants in their native habitats4. Across two contrasting field sites and five growing seasons (2021-2025), we phenotyped more than 3,000 A. thaliana plants and generated >1,600 matching transcriptomes. The resulting >30,000 quantitative trait measurements provide a unique opportunity to link climate fluctuations with plant traits and gene expression. Seasons characterized by extreme temperature anomalies directly influenced plant traits, and climatic variables together explained up to 17% of phenotypic variation. In situ transcriptomes carried clear temperature and local environmental signatures, closely matching temperature-response programs known from the laboratory. Leveraging paired per-plant transcriptomes and phenotypes, we applied machine learning to predict regulators of climate-relevant and other plant traits under natural conditions. The models recovered canonical thermomorphogenesis regulators, including PHYTOCHROME INTERACTING FACTOR 4 (PIF4)5,6, providing ecological evidence that temperature signaling pathways defined in controlled environments operate in the wild, and expanded this regulatory landscape by identifying hormonal receptors, signaling components, and previously uncharacterized genes, some of which we functionally validated. Together, this work demonstrates that landscape transcriptomics, by integrating natural field transcriptomes with phenotypes, and thus, capturing environmental and regulatory states, enables the predictive identification of genetic regulators of temperature responses and broader plant traits. This makes landscape transcriptomics a scalable framework for climate-aware functional genomics in plants.

plant biology↗