Biology subjects

Szymanski, J. J.

Publications and source records attributed to Szymanski, J. J..

5 recordsLinked to original sources

bioRxiv2026

Data-informed modelling captures metabolic reprogramming and reveals branch points mediating cold stress response and growth trade-offs in rice

Understanding stress-induced metabolic reprogramming in crop plants can inform breeding strategies and support the development of stress-resilient varieties. Genome-scale metabolic modelling has shown promise in elucidating network-level responses to changing environments, yet as an optimality-based approach it relies on the definition of an objective function, which is far from trivial for non-optimal conditions. To address this uncertainty, we used a time-resolved, data-informed metabolic model of rice (Oryza sativa L.) cold stress response as a test case, and explored two complementary approaches. We used sampling of the solution space combined with machine learning to identify reactions and pathways best characterizing the stress-induced metabolic shift, and used this information to perform Pareto analysis, placing growth and a stress-related objective in competition. This trade-off analysis identified key branch points in carbohydrate, amino acid, phenylpropanoid, nucleotide, and fatty acid biosynthesis, where resource reallocation towards stress-protection comes at the expense of growth. It further revealed differential flux modes across subcellular compartments and shifts in reducing equivalent provision as distinguishing features of the stress response. Together, these results provide a mechanistic understanding of the metabolic trade-offs and branch points governing cold stress response, and identify potential targets to optimize the cold response-growth trade-off in rice.

plant biology↗

bioRxiv2025

The complex molecular basis of enhanced stress resilience in extreme drought-tolerant Arabis grassland species

Background and AimsPlant species in competitive meadows must tolerate extreme stress, yet the mechanisms underlying resilience remain poorly understood. Arabis nemorensis, an endangered selfing species of Euro- pean floodplain grasslands, experiences both flooding and drought and hybridizes with its close relative, A. sagittata. We investigated how these species differ in drought survival and the molec- ular basis of their responses. MethodsSympatric lineages of A. nemorensis and A. sagittata were compared in a controlled dry-down experiment, complemented by transcriptome and small RNA profiling, and machine-learning anal- ysis of cis-regulatory motifs. Key ResultsBoth species wilted at 5% soil moisture, but A. sagittata recovered more effectively (90% vs. 50%). This difference was not explained by a major QTL, suggesting a polygenic basis. Transcrip- tome profiling revealed stronger induction in A. sagittata (6,359 vs. 5,571 differentially expressed genes). Small RNA analysis identified species-specific regulation of miR408, a conserved drought regulator. Machine-learning identified 307 sequence motifs predictive of stress-responsive expres- sion, with motif distributions indicating distinct regulatory networks. ConclusionsThis study reveals the polygenic and regulatory complexity underlying divergent drought resili- ence strategies in the closely related species thriving in grassland environments. Arabis nemorensis and its close relative A. sagittata co-occur in a floodplain meadow exposed to flooding and drought. In dry-down experiments, A. sagittata recovered more effectively than A. nemorensis. Transcriptome and small RNA analyses revealed stronger stress responses in A. sagittata, including regulation of miR408. These differences result from different regulatory networks and have a polygenic basis.

plant biology↗

bioRxiv2025

panomiX: Investigating Mechanisms Of Trait Emergence Through Multi-Omics Data Integration

Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. panomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.

bioinformatics↗

bioRxiv2022

Genomic patterns of malignant peripheral nerve sheath tumour (MPNST) evolution correlate with clinical outcome and are detectable in cell-free DNA

Malignant peripheral nerve sheath tumour (MPNST) is an aggressive soft-tissue sarcoma that arises in peripheral nerves. MPNST occurs either sporadically or in people with neurofibromatosis type 1 (NF1), a common cancer predisposition syndrome caused by germline pathogenic variants in NF1. Although MPNST is the most common cause of death and morbidity for individuals with NF1, the molecular underpinnings of MPNST pathogenesis remain unclear. Here, we report the analysis of whole-genome sequencing, multi-regional exome sequencing, transcriptomic and methylation profiling data for 95 MPNSTs and precursor lesions (64 NF1-related; 31 sporadic) from 77 individuals. Early events in tumour evolution include biallelic inactivation of NF1 followed by inactivation of CDKN2A and in some cases also TP53 and polycomb repressive complex 2 (PRC2) genes. Subsequently, both sporadic and NF1-related MPNSTs acquire a high burden of somatic copy number alterations (SCNAs). Our analysis revealed distinct pathways of tumour evolution and immune infiltration associated with inactivation of PRC2 genes and H3K27 trimethylation (H3K27me3) status. Tumours with loss of H3K27me3 evolve through extensive chromosomal losses with retention of chromosome 8 heterozygosity followed by whole genome doubling and chromosome 8 amplification. These tumours show lower levels of immune cell infiltration with low cytotoxic activity and low expression of immune checkpoints. In contrast, tumours with retention of H3K27me3 evolve through extensive genomic instability in the absence of recurrent alterations and exhibit an immune cell-rich phenotype. Specific SCNAs detected in both tumour samples and cell-free DNA (cfDNA) act as a surrogate for loss of H3K27me3 and immune infiltration, and predict prognosis. Our results suggest that SCNA profiling of tumour or cfDNA could serve as a biomarker for early diagnosis and to stratify patients into prognostic and treatment-related subgroups.

cancer biology↗

bioRxiv2020

Hello darkness, my old friend: 3-Ketoacyl-Coenzyme A Synthase4 is a branch point in the regulation of triacylglycerol synthesis in Arabidopsis by re-channeling fatty acids under carbon starvation

Due to their sessile lifestyle, plants have evolved unique mechanisms to deal with environmental challenges. Under stress, plant lipids are important as alternative sources of carbon and energy when sugars or starch are limited. Here, we applied combined heat and darkness and extended darkness to a panel of [~] 300 Arabidopsis accessions to study lipid remodeling under carbon starvation. Natural allelic variation at 3-KETOACYL-COENZYME A SYNTHASE4 (KCS4), a gene encoding for an enzyme involved in very long chain fatty-acid (VLCFA) synthesis, underlies a differential accumulation of polyunsaturated triacylglycerols (puTAGs) under stress. Ectopic expression in yeast and plants proved that KCS4 is a functional enzyme localized in the ER with specificity for C22 and C24 saturated acyl-CoA. Allelic mutants and transient overexpression in planta revealed the differential role of KCS4 alleles in VLCFA synthesis and wax coverage, puTAG accumulation and biomass. Moreover, the region harboring KCS4 is under high selective pressure and allelic variation at KCS4 correlated with environmental parameters from the locales of Arabidopsis accessions. Our results provide evidence that KCS4 plays a decisive role in the subsequent fate of fatty acids released from chloroplast-membrane lipids under carbon starvation. This work sheds light on both plant response mechanisms and the evolutionary events shaping the lipidome under carbon starvation. One sentence summaryNatural variation at KCS4 underlies a differential accumulation of polyunsaturated triacylglycerols under carbon starvation, by acting as a regulatory branch point in the fate of fatty acids.

genetics↗