Biology subjects

Mehrem, S. L.

Publications and source records attributed to Mehrem, S. L..

9 recordsLinked to original sources

bioRxiv2026

SST-MAE: Learning Spectral-Spatio-Temporal Representations from Plant Hyperspectral Time Series to Discover Complex Genotype-Phenotype Relations

Understanding the link between genetic variation and observable traits is key to crop breeding. Hyperspectral imaging captures physiological and biochemical profiles, but current supervised methods require costly trait annotations and treat each observation as a static snapshot, ignoring the temporal dynamics of plant development. We introduce SST-MAE, a self-supervised framework that learns genotype-discriminative representations from plant hyperspectral developmental trajectories, without requiring phenotypic labels. The model learns to reconstruct masked information, capturing multiple growth trajectories. Validated on 194 field-grown lettuce genotypes across eight time points, the frozen encoder serves as a feature extractor for downstream genotype classification. SST-MAE outperforms raw spectral and linear baselines, achieving AUROC > 0.89 for anthocyanin pigmentation SNPs and 0.77 for leaf serration. The learned features are highly label-efficient, attaining near-full performance with only 30-50% of labeled data, offering a scalable pathway toward high-throughput genetic screening from image-based phenotypes.

bioinformatics↗

bioRxiv2026

DeepPheno: A Deep Learning Framework for Linking Hyperspectral Imaging and SNP Genotypes in Lettuce

While whole-genome sequencing captures millions of single nucleotide polymorphisms (SNPs) and hyperspectral imaging (HSI) enables non-destructive plant phenotyping, integrating these modalities to link genotype to phenotype remains challenging due to their high dimensionality and non-linearity. This study presents DeepPheno a deep learning framework that predicts SNP genotypes from HSI data, using model predictability as a proxy for genotype-phenotype association. HSI data were acquired from 194 lettuce genotypes under field conditions. HSI data patches (20x20 pixels x 224 spectral bands) were used to train a hybrid CNN to predict the variant of a specific SNP. The framework was validated on SNPs with known phenotypic effects (anthocyanin, leaf serration, pale pigmentation), achieving high predictive performance (AUC ranging from 0.806 to 0.935), whereas models trained on randomly shuffled labels performed at chance (mean AUC {approx} 0.51). Extending the workflow to 50 randomly selected putatively neutral SNPs, most yielded low predictability, but two showed high performance (AUC > 0.76), suggesting uncharacterized genotype-phenotype links. Explainable AI, including SHAP and Grad-CAM, identified relevant spectral and spatial features driving these predictions, particularly the green and red-edge wavelengths associated with pigment dynamics and leaf structure. These results establish a framework for understanding complex genotype-phenotype interactions in plants and extracting these links from HSI data without predefining the exact trait values. It provides an avenue for high-throughput trait discovery and description and extends the integration of image-based phenomics with plant genetics.

plant biology↗

bioRxiv2026

Spectral Phenotyping Reveals Time-Specific QTLs in Field-Grown Lettuce

Lettuce (Lactuca sativa) is an important field crop, but our understanding of its phenotypic variation and underlying genetics under natural field conditions remains limited, posing challenges for identifying effective crop breeding targets. Longitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions. In this study, we used hyperspectral imaging to assess the phenotypic variation of almost 200 different field-grown lettuce varieties, following the same plants from just after seedling- to flowering-stage. With automated image processing, we extracted a wide range of spectral phenotypes related to metabolite content, growth efficiency, and environmental stress responses, creating a multi-dimensional time-resolved data set. Principal component analysis (PCA) revealed the major axes of spectral variation over time, and highlighted differences in spectral patterns among lettuce genotypes. Integrating on-site weather data, we modelled GxE interactions of reflectance, revealing regions of the lettuce vegetation spectrum that are primarily shaped by genotype and/or environment. We estimated phenotypic plasticity in response to time, temperature and rainfall using best linear unbiased predictions (BLUPs), capturing genotype-specific developmental trajectories and responses to the environment. We used genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs) of PC-based, single and BLUP-based phenotypes, disentangling the genetic architecture of spectral lettuce phenotypes from major axes of variation down to single wavelength spectral plasticity. These findings provide new insights into the genome-wide genetic regulation and dynamics of spectral phenotypes in field grown lettuce.

plant biology↗

bioRxiv2025

A lettuce receptor-like kinase recognizes the highly conserved heptapeptide motif within microbial NEP1-like proteins

Plants rely on cell surface immune receptors to detect microbial patterns and initiate effective defense responses. Although the Asteraceae family is one of the largest and economically important plant groups, little information is available about its pattern-triggered immunity signaling. Cultivated lettuce (Lactuca sativa L.) recognizes a 24-amino acid peptide (nlp24) from necrosis- and ethylene-inducing peptide 1-like proteins (NLPs) found in bacteria, fungi, and oomycetes. Here, we perform an extensive characterization of nlp24-induced immune responses in lettuce and identify the LETTUCE nlp24 RECEPTOR (LNR) as the leucine-rich repeat receptor-like kinase mediating its recognition. Remarkably, nlp24 recognition in lettuce and subsequent activation of defenses strongly depend on the conserved heptapeptide motif (GHRHDWE). Structural modeling-guided mutagenesis experiments suggest that residues in the nlp24 heptapeptide interact with a hydrophobic pocket in the LNR solenoid structure. Divergent ligand specificities and the absence of sequence homology between LNR and Arabidopsis nlp24-recognizing receptor indicate that the NLP recognition in lettuce and Arabidopsis emerged independently, through convergent evolution. Our phylogenetic analysis shows that LNR is closely related to Arabidopsis MIK2 (MALE DISCOVERER 1-INTERACTING RECEPTOR-LIKE KINASE 2), but belongs to a distinct, Asteraceae-specific monophyletic subgroup that has undergone a significant expansion in Lactuca. Our findings provide insights into the mechanisms of pattern-triggered immunity in lettuce and the fast evolution of its immune receptor repertoire. On the translational side, our findings open opportunities for the crop defense improvement via interfamily transfer. Significance statementUnderstanding pathogen recognition in crops is key to improving disease resistance. Our study identified the cell surface immune receptor in cultivated lettuce that senses the nlp24 pattern derived from secreted proteins (NLPs) of prokaryotic and eukaryotic microbial pathogens. Unlike the previously characterized receptor from Arabidopsis (RLP23), the lettuce nlp24 receptor (LNR) detects the deeply conserved heptapeptide motif of NLP proteins that is required for host cell lysis. Transfer of the LNR receptor to a Solanaceous species conferred quantitative resistance to the oomycete pathogen Phytophthora capsici. Our findings advance the understanding of pattern-triggered immunity in a major leafy crop and highlight LNR as a promising receptor for broad-spectrum plant resistance engineering.

plant biology↗

bioRxiv2025

GreenLeafVI: A FIJI plugin for high-throughput analysis of leaf chlorophyll content

Chlorophyll breakdown is a central process during plant senescence or stress responses and leaf chlorophyll content is therefore a strong predictor of plant health. Chlorophyll quantification can be done in several ways, most of which are time-consuming or require specialized equipment. A simple alternative to these methods is the use of image-based chlorophyll estimation, which uses the color values in RGB images to calculate colorimetric visual indexes as a measure for the leaf chlorophyll content. Image-based chlorophyll measurement is non-destructive and, apart from a digital camera, requires no specialized equipment. Here, we developed the ImageJ plugin GreenLeafVI that facilitates high-throughput image analysis for measuring leaf chlorophyll content. Our plugin offers the option to white-balance images to decrease variation between images and has an optional background removal step. We show that this method can reliably quantify leaf chlorophyll content in a variety of plant species. In addition, we show that image-based chlorophyll quantification can replicate GWAS results based on traditional chlorophyll extraction methods, showing that this method is highly accurate.

plant biology↗

bioRxiv2025

Exploring phenotypic and genetic variation in Lactuca with GWAS in L. sativa and L. serriola

Crop wild relatives provide valuable insights into trait diversity and the genetic basis of agronomic traits. In the genus Lactuca, domesticated lettuce (Lactuca sativa) and its wild progenitor, Lactuca serriola, have been extensively studied, yet broader wild species remain underrepresented. Here, we present a phenotypic dataset of 550 Lactuca accessions, including 20 wild relatives, capturing plant morphology, pigmentation, and pathogen resistance traits derived from images and genetic resource collections. To investigate the genetic basis of these traits, we used a jointly processed SNP set for L. sativa and L. serriola, applying an iterative two-step GWAS approach, enabling the dissection of multiple loci per trait. We identified both known and novel QTLs associated with anthocyanin accumulation, leaf morphology, and pathogen resistance in L. sativa and L. serriola. Importantly, we identified L. serriola-specific QTLs undetected in L. sativa, revealing unique genetic architectures underlying anthocyanin biosynthesis and leaf morphology in the wild progenitor. These findings expand the knowledge of Lactuca beyond cultivated varieties, highlighting the potential of wild species for breeding applications. Our dataset and results provide a foundation for further investigations into the evolutionary and agronomic significance of Lactuca diversity.

plant biology↗

bioRxiv2024

From aerial drone to QTL: Leveraging next-generation phenotyping to reveal the genetics of color and height in field-grown Lactuca sativa

In recent years, the automation of genotyping has significantly enhanced the efficiency of genome-wide association studies. As a result, phenotyping rather than genotyping is now the rate-limiting step, especially in field experiments. For this reason, there is a strong need to further automate in-field phenotyping. Here we present a GWAS study on 194 field-grown accessions of lettuce (Lactuca sativa). These accessions were non-destructively phenotyped at two time points 15 days apart using an unmanned aerial vehicle. Our high throughput phenotyping approach integrates an RGB camera, a multispectral camera to measure the reflectance at 5 wavelengths (blue, green, red, red edge, near-infrared), and precise height estimation. We used the mean and other descriptives such as median, quantiles, minimum and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using this approach, we confirm several previously described QTLs, now in populations grown under field conditions, and identify several new QTLs for plant-height and color.

plant biology↗

bioRxiv2024

Natural variation in seed coat color in lettuce and wild Lactuca species

Seed coat color is a well described trait in lettuce (Lactuca sativa), varying from black to pale white pigmentation. In this study, we delve into seed coat color variation of several species within the Lactuca genus, encompassing L. sativa and 15 wild varieties, offering broader insights into the diversity of this trait. To capture seed coat color quantitatively, we use grey pixel values from publicly available images, enabling us to measure seed coat color as a continuous trait across the genus. Darker seed coats predominate within the Lactuca genus, with L. sativa displaying a distinctive bimodal distribution of black and white seed coats. Lactuca virosa exhibits the darkest seed coat coloration and less variation, while Lactuca saligna and Lactuca serriola display lighter shades and greater variability. To identify the polymorphic loci underlying the observed variation we performed GWAS on seed coat color in both L. sativa and L. serriola. For L. sativa, we confirmed the one known major QTL linked to black and white seed coat color, which we reproduce in two independent, published genotype collections (n=129, n=138). Within the same locus, we identify additional candidate genes associated with seed coat color. For L. serriola, GWAS yielded several minor QTLs linked to seed coat color, harboring candidate genes predicted to be part of the anthocyanin pathway. These findings highlight the phenotypic diversity present within the broader Lactuca genus and provide insights into the genetic mechanisms governing seed coat coloration in both cultivated lettuce and its wild relatives.

plant biology↗

bioRxiv2024

Lactuca super-pangenome reduces bias towards reference genes in lettuce research

Breeding of lettuce (Lactuca sativa L.), the most important leafy vegetable worldwide, for enhanced disease resistance and resilience relies on multiple wild relatives to provide the necessary genetic diversity. In this study, we constructed a super-pangenome based on four Lactuca species (representing the primary, secondary and tertiary gene pools) and comprising 474 accessions. We include 68 newly sequenced accessions to improve cultivar coverage and add important foundational breeding lines. With the super-pangenome we find substantial presence/absence variation (PAV) and copy-number variation (CNV). Functional enrichment analyses of core and variable genes show that transcriptional regulators are conserved whereas disease resistance genes are variable. PAV-genome-wide association studies (GWAS) and CNV-GWAS are largely congruent with single-nucleotide polymorphism (SNP)-GWAS. Importantly, they also identify several major novel quantitative trait loci (QTL) for resistance against Bremia lactucae in variable regions not present in the reference lettuce genome. The usability of the super-pangenome is demonstrated by identifying the likely origin of non-reference resistance loci from the wild relatives Lactuca serriola, Lactuca saligna and Lactuca virosa. The provided methodology and data provide a strong basis for research into PAVs, CNVs and other variation underlying important biological traits of lettuce and other crops.

plant biology↗