Author: slquinlan

Plant Direct. 2026 May 6;10:e70170. doi: 10.1002/pld3.70170. eCollection 2026 May.

ABSTRACT

Peanut reproduction is foundational for crop yield, breeding, and evolution. However, gene regulation underlying peanut flowering pattern and timing has received limited attention. Cultivated peanut (Arachis hypogaea L.) shows two distinct flowering patterns between two subspecies, with ssp. hypogaea lacking flowers on the main stem and ssp. fastigiata having them. Understanding the gene regulatory networks that control peanut flowering will inform the genetic pathways impacting peanut reproduction, phenology, and yield. To this end, we measured whole-transcriptome gene expression of leaves and shoot tips (meristem) at six plant growth stages from Tifrunner, a peanut cultivar belonging to ssp. hypogaea, and GT-C20, a peanut germplasm belonging to ssp. fastigiata. Overall gene expression was distinct between the two genotypes in both tissue types. Flowering regulators including AhFT, AhSOC1, AhAGL42, and AhSPL3 were differentially expressed in both the main and lateral stem at the time of flowering initiation (T3-first bloom). This indicates that positive regulation of these flowering regulators drives the distinct pattern of flowering on the main stem in GT-C20. Meanwhile, the differential expression of two RING-finger E3 ubiquitin ligases was identified between the two genotypes, indicating that the PAF1-complex (PAF1C) may contribute to the lack of flowering on the main stem of Tifrunner. Gene co-expression network analysis indicates that gibberellic acid (GA) and jasmonic acid (JA) pathways are involved in reproductive regulation. These results provide insight into how flowering physiology is differentially controlled between the two peanut subspecies and provide a launching point for additional research in peanut floral development.

PMID:42099581 | PMC:PMC13147162 | DOI:10.1002/pld3.70170

Nat Genet. 2026 May 5. doi: 10.1038/s41588-026-02598-8. Online ahead of print.

ABSTRACT

Pangenomes are increasingly important for harnessing crop genetic diversity, yet their resolution and utility are often limited by insufficient sampling of high-quality genome assemblies. Here we present a population-level watermelon super-pangenome constructed from 138 reference-grade assemblies, including 135 newly generated genomes representing all seven species. This super-pangenome captures approximately 1 million structural variants (SVs), enabling accurate variant genotyping across 914 accessions. Broader sampling within the pangenome provides insights into watermelon genome evolution and the origin of cultivated watermelon. Incorporating SVs into genome-wide association studies improves mapping resolution and reveals a copy number variant upstream of ClFCI1 that regulates flesh color intensity in a dosage-dependent manner. Leveraging this comprehensive variation map, we developed high-accuracy genomic prediction models for 18 agronomic traits. Together, these findings and genomic resources establish a foundation for dissecting complex traits and accelerating precision breeding in watermelon, while offering a valuable model for SV-resolved pangenomics in crops.

PMID:42086854 | DOI:10.1038/s41588-026-02598-8

PLoS Biol. 2026 May 5;24(5):e3003561. doi: 10.1371/journal.pbio.3003561. Online ahead of print.

ABSTRACT

Cryptococcus neoformans is an environmental pathogen that remodels its cellular physiology to survive within mammals and, in susceptible hosts, cause life-threatening meningoencephalitis. Of the many distinctions between the external environment and mammalian tissues, CO2 concentration in the host is two orders of magnitude higher than in the environment and represents a critical stress for C. neoformans. C. neoformans strains that do not replicate at host CO2 concentrations are less virulent in mouse models of infection, further supporting CO2 tolerance as a virulence trait. To further understand the genetic determinants of C. neoformans CO2 tolerance, we performed a near genome-wide screen for deletion mutants with altered CO2 fitness using a competitive growth assay. A total of 301 of 4,692 deletion mutants showed altered CO2 tolerance (245 reduced fitness; 56 increased fitness) demonstrating the global effect of host CO2 on C. neoformans physiology. Based on this data set as well as a metabolomic analysis of C. neoformans adaptation to host CO2, we show that remodeling of central carbon metabolism, oxidative stress buffering, and membrane homeostasis represent an integrated response to CO2 stress that is mediated in part by the TOR-Ypk1 signaling axis. We propose that CO2-induced capsule formation leads to reduced cellular glucose which, in turn, triggers remodeling of central carbon metabolism toward utilization of alternative carbon sources and increased mitochondrial respiration/reactive oxygen generation. Thus, these data provide a near genome-wide profile of the genetic determinants of C. neoformans CO2 tolerance as well as a model for how this important environmental human fungal pathogen alters its physiology to proliferate in the host.

PMID:42085498 | DOI:10.1371/journal.pbio.3003561

Theor Appl Genet. 2026 May 5;139(6):148. doi: 10.1007/s00122-026-05240-7.

ABSTRACT

Although herbicide resistance in Setaria is rampant and cosmopolitian across four herbicide families, we encountered little evidence of target-site resistance, indicating diverse non-target mechanisms of metabolizing, sequestering, and overwhelming herbicides. Setaria viridis is a cosmopolitan weed and model genetic system with increasing reports of resistance to multiple classes of herbicides. Our goal was to assess the herbicide resistance and allelic diversity in herbicide target genes in a collection of Setaria genotypes from North America and Eurasia, and identify the occurrence of novel and known target-site mutations that led to resistance. A total of 214 Setaria genotypes were exposed to commonly used herbicides that inhibit specific genes: herbicide action class (HRAC) group 1 herbicides targeting acetyl-CoA carboxylase (ACCase), HRAC 2 targeting acetolactate synthase (ALS), HRAC 9 targeting 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase, and HRAC 10 targeting glutamine synthetase. ACCase and ALS genes in 53 accessions were PCR-amplified and sequenced. Whole-genome sequencing reads covering the target genes were analyzed for an additional 98 genotypes. Herbicide trials showed that 30% of our accessions set seed following application of at least one herbicide, and 13 accessions were resistant to multiple classes of herbicides. Although there were numerous SNPs, including some known to lead to resistance, in our target genes, SNPs found predominantly in herbicide-resistant genotypes were largely intronic or synonymous. A small number of amino acid substitutions in ALS and ACCase indicated potential and incomplete resistance to HRAC 1 and 2 herbicides, but no SNPs putatively associated with herbicide resistance were identified in the other 6 target-site genes. The broader pattern of herbicide resistance in S. viridis is likely driven by non-target mutations that detoxify or compartmentalize applied herbicides.

PMID:42084731 | DOI:10.1007/s00122-026-05240-7

J Food Prot. 2026 Apr 30:100799. doi: 10.1016/j.jfp.2026.100799. Online ahead of print.

ABSTRACT

Light exposure is an important factor influencing the Escherichia coli survival in agricultural soils. This study evaluated the combined effects of soil moisture and light exposure on the survival of E. coli in agricultural soil under a controlled diurnal environment. For this, soil mesocosms were inoculated with E. coli TVS353 and maintained at three moisture levels (100%, 75%, and 50% field capacity (FC)) and two light conditions (diurnal and complete dark), with a 14 h day at 25 ℃ and a 10 h night at 15 ℃. Soil samples were enumerated for E. coli on days 0, 1, 3, 7, 14, 28, 35, 56, and every 7 days thereafter, and survival dynamics were analyzed using linear mixed-effect analysis and a biphasic microbial survival model. Across all treatments, E. coli declined significantly over time (p <0.0001). Overall, E. coli survived up to 140 days under higher soil moisture levels (100% and 75% FC) and in dark conditions, whereas survival was 63 to 70 days at 50% FC in the diurnal environment. Biphasic survival modeling revealed higher initial inactivation rate constants under low-moisture and low-light exposure conditions. This study represents one of the first attempts to quantitatively assess the effect of light exposure on E. coli survival in agricultural soils across varying moisture levels.

PMID:42069220 | DOI:10.1016/j.jfp.2026.100799

PLoS Pathog. 2026 Apr 30;22(4):e1014198. doi: 10.1371/journal.ppat.1014198. Online ahead of print.

ABSTRACT

Pantoea ananatis, the causative agent of onion center rot, encounters potent antimicrobial thiosulfinates, volatile organosulfur compounds released from damaged Allium tissue during pathogen-induced necrosis. The allicin tolerance (alt) gene cluster allows P. ananatis to overcome this chemical barrier. We demonstrate that AltR, a TetR-family transcriptional repressor, specifically regulates expression of the alt cluster and thus thiosulfinate tolerance in vitro and fitness in vivo. We identified a putative AltR binding box both in the altR promoter and elsewhere in the alt cluster, show that AltR-mediated repression is relieved in response to thiosulfinates. Using cysteine to serine substitutions, we demonstrate that AltR Cys100 is essential for thiosulfinate-responsive de-repression, while other AltR cysteine residues tune responsivity. Strains expressing AltR alleles with reduced thiosulfinate responsivity have reduced fitness in planta. Our findings uncover a regulatory mechanism by which a plant antimicrobial secondary metabolite acts as an environmental cue to modulate bacterial gene expression, enabling pathogen survival and virulence.

PMID:42060678 | DOI:10.1371/journal.ppat.1014198

Plant Phenomics. 2025 Oct 27;7(4):100126. doi: 10.1016/j.plaphe.2025.100126. eCollection 2025 Dec.

ABSTRACT

Unmanned aerial systems (UAS) are reliable tools for field phenotyping, enabling rapid, large-scale, and cost-effective data collection to support breeding programs. However, many UAS-based approaches rely on manual data processing, limiting scalability and efficiency. This study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN). SAM auto-mask generator mode was used to identify field extent and orientation, while SAM interactive mode enabled individual plot segmentation using auto-generated point prompts. Terrain points automatically sampled near each plot were used to model the ground surface and compute the canopy height model, allowing CH estimations at the plot level. CH estimations showed strong agreement with manual measurements (R² ​= ​0.78, RMSE ​= ​3 ​cm, MAPE ​= ​10 ​%). For MP and GH estimation, three pre-trained CNN models (AlexNet, ResNet18, and EfficientNet-B0) were evaluated, with AlexNet achieving the highest accuracy (89 ​% for GH, 83 ​% for MP). To assess the feasibility of using these HTP-derived estimations in plant breeding, quantitative trait loci (QTL) analysis was performed, identifying major-effect loci associated with these traits. The results were consistent with conventional QTL mapping methods, demonstrating that UAS-based phenotyping provides reliable trait data for genetic studies in peanut breeding. Overall, our deep learning-based data processing workflow minimizes manual efforts, providing an efficient and scalable approach that can accelerate genetic studies and trait selection in large-scale breeding programs.

PMID:42040996 | PMC:PMC13109298 | DOI:10.1016/j.plaphe.2025.100126

Plant Dis. 2026 Apr 27. doi: 10.1094/PDIS-01-26-0221-SC. Online ahead of print.

ABSTRACT

Cotton bacterial blight (CBB), caused by Xanthomonas citri pv. malvacearum (Xcm), reemerged in U.S. cotton production without evidence of new pathogen races. To test whether resistant cotton can act as an inoculum source, we conducted replicated field trials over two seasons using Xcm strains with distinct spontaneous antibiotic resistance markers facilitating source tracking from inoculated resistant and susceptible seed embedded within rows of susceptible and uninoculated plants. Xcm was recovered from uninoculated susceptible plants in both years, originating from both resistant and susceptible sources, with recovery highest early in the season and declining over time. Most isolations were from asymptomatic leaves, and rare seed-to-seed transmission from resistant plants was confirmed (0.13%). These findings provide field-based evidence that resistant cotton can asymptomatically harbor Xcm and potentially contributing to pathogen persistence.

PMID:42037266 | DOI:10.1094/PDIS-01-26-0221-SC

Epidemics. 2026 Apr 9;55:100914. doi: 10.1016/j.epidem.2026.100914. Online ahead of print.

ABSTRACT

SARS-CoV-2, the virus responsible for COVID-19, emerged in late 2019 and rapidly spread worldwide. Inferring transmission direction between epidemiologically linked cases is an important component of outbreak investigation, yet symptom-onset-based heuristics can become unreliable when onset differences are small due to incubation variability, reporting noise, and asymptomatic infections. Using a detailed contact-tracing dataset, we develop Pseudo-Time Reconstruction for Epidemic (PTRE), a network-informed analytical framework that integrates symptom timing, individual-level information, and contact network structure to induce a relative ordering of cases. We apply this framework to regional COVID-19 contact-tracing data and evaluate directional discrimination across onset-gap regimes. We further validate PTRE through simulation studies with known ground-truth transmission directions on diverse network topologies. Our results show a regime-dependent pattern: PTRE aligns with onset-based ordering when temporal separation is large, while structural connectivity provides a complementary directional signal when onset differences are minimal. These findings highlight both the potential and the limitations of integrating network information to enhance directional discrimination under temporal ambiguity.

PMID:41985381 | DOI:10.1016/j.epidem.2026.100914

Plant J. 2026 Apr;126(1):e70856. doi: 10.1111/tpj.70856.

ABSTRACT

Caterpillars and beetles are among the most economically damaging defoliating insects, and their economic damage is predicted to increase in the coming decades. Hence the use of genetically derived resistance to supplement other pest control strategies is warranted. In soybean (Glycine max (L.) Merr.), a major determinant for resistance is the quantitative trait locus, QTL-M. Glyma07g14530, the gene underlying QTL-M, encodes a feeding-inducible flavonol 3-O-glycosyltransferase (F3GlcT or UGT78D2) that glucosylates kaempferol, as well as quercetin, myricetin, and isorhamnetin. The resistant allele has a premature stop codon in it, thus preventing the glucosylation and sequestration of flavonols in the vacuole, leading to a concomitant accumulation of proanthocyanidins and manifestation of resistance. Expressing the dominant (susceptible) allele in resistant plants restores susceptibility, and silencing the susceptible allele results in resistance. The discovery and characterization of GmF3GlcT helps clarify the role of flavonoids in resistance to leaf-chewing insects and facilitates the development of insect-resistant cultivars that ultimately can lower production costs and reduce insecticide applications.

PMID:41985162 | PMC:PMC13082823 | DOI:10.1111/tpj.70856