Elife. 2025 Mar 18;13:RP96892. doi: 10.7554/eLife.96892.

ABSTRACT

Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.

PMID:40100251 | DOI:10.7554/eLife.96892

Protein Sci. 2025 Apr;34(4):e70091. doi: 10.1002/pro.70091.

ABSTRACT

Increasing insights into how sequence motifs in intrinsically disordered regions (IDRs) provide functions underscore the need for systematic motif detection. Contrary to structured regions where motifs can be readily identified from sequence alignments, the rapid evolution of IDRs limits the usage of alignment-based tools in reliably detecting motifs within. Here, we developed SHARK-capture, an alignment-free motif detection tool designed for difficult-to-align regions. SHARK-capture innovates on word-based methods by flexibly incorporating amino acid physicochemistry to assess motif similarity without requiring rigid definitions of equivalency groups. SHARK-capture offers consistently strong performance in a systematic benchmark, with superior residue-level performance. SHARK-capture identified known functional motifs across orthologs of the microtubule-associated zinc finger protein BuGZ. We also identified a short motif in the IDR of S. cerevisiae RNA helicase Ded1p, which we experimentally verified to be capable of promoting ATPase activity. Our improved performance allows us to systematically calculate 10,889 motifs for 2695 yeast IDRs and provide it as a resource. SHARK-capture offers the most precise tool yet for the systematic identification of conserved regions in IDRs and is freely available as a Python package (https://pypi.org/project/bio-shark/) and on https://git.mpi-cbg.de/tothpetroczylab/shark.

PMID:40100159 | DOI:10.1002/pro.70091

Protein Sci. 2025 Apr;34(4):e70084. doi: 10.1002/pro.70084.

ABSTRACT

The Protein Data Bank (PDB) is the primary global repository for experimentally determined 3D structures of biological macromolecules and their complexes with ligands, proteins, and nucleic acids. PDB contains over 47,000 unique small molecules bound to the macromolecules. Despite the extensive data available, the complexity of small-molecule data in the PDB necessitates specialized tools for effective analysis and visualization. PDBe has developed a number of tools, including PDBe CCDUtils (https://github.com/PDBeurope/ccdutils) for accessing and enriching ligand data, PDBe Arpeggio (https://github.com/PDBeurope/arpeggio) for analyzing interactions between ligands and macromolecules, and PDBe RelLig (https://github.com/PDBeurope/rellig) for identifying the functional roles of ligands (such as reactants, cofactors, or drug-like molecules) within protein-ligand complexes. The enhanced ligand annotations and data generated by these tools are presented on the novel PDBe-KB ligand pages, offering a comprehensive overview of small molecules and providing valuable insights into their biological contexts (example page for Imatinib: https://pdbe.org/chem/sti). By improving the standardization of ligand identification, adding various annotations, and offering advanced visualization capabilities, these tools help researchers navigate the complexities of small molecules and their roles in biological systems, facilitating mechanistic understanding of biological functions. The ongoing enhancements to these resources are designed to support the scientific community in gaining valuable insights into ligands and their applications across various fields, including drug discovery, molecular biology, systems biology, structural biology, and pharmacology.

PMID:40100137 | DOI:10.1002/pro.70084

J Exp Bot. 2025 Mar 18:eraf122. doi: 10.1093/jxb/eraf122. Online ahead of print.

ABSTRACT

Linking genotype and phenotype is a fundamental challenge in biology. In this respect, machine learning is playing a pivotal role in systems biology. As a central phenotypic trait, fruit development and its relative growth rate (RGR) result from interactions between gene regulation, metabolism and environment. In the present study, we carried out a multispecies transcriptomic analysis of nine different fruits. To illustrate fruit transcriptomes, transcripts were first compared using multivariate methods, revealing main similar profiles. They were then used as variables to predict four growth traits, i.e. RGR, developmental progress, fruit weight and protein content, using generalised linear models (GLMs) to decipher the mechanisms involving gene expression in development. The predictions were very satisfactory despite disparities when the model did not include the entire panel of fruit species. Based on orthogroups derived from BLAST and annotated consensus sequences from gene ontology (GO) terminology, variables annotated for metabolic processes, especially those involving cell wall carbohydrates and proteins, were found to be the most effective in predicting growth. In addition, predictions were improved for RGR when introducing a seven-day lag between transcript contents and growth traits, suggesting the necessity of considering the proteins produced to enhance phenotypic trait predictions. These original results showed that growth traits can be predicted very well with GLMs based on orthogroups from multi-species transcriptomes.

PMID:40099514 | DOI:10.1093/jxb/eraf122

Expert Opin Drug Discov. 2025 Mar 18. doi: 10.1080/17460441.2025.2481262. Online ahead of print.

ABSTRACT

INTRODUCTION: Drug discovery is a complex and multifaceted process driven by scientific innovation and advanced technologies. Next-Generation Sequencing (NGS) platforms, encompassing both short-read and long-read technologies, have revolutionized the field by enabling the high-throughput and cost-effective analysis of DNA and RNA molecules. Continuous advancements in NGS-based technologies have enabled their seamless integration across preclinical and clinical workflows in drug discovery, encompassing early-stage drug target identification, candidate selection, genetically stratified clinical trials, and pharmacogenetic studies.

AREA COVERED: This review provides an overview of the current and potential applications of NGS-based technologies in drug discovery and development process, including their roles in novel drug target identification, high-throughput screening, clinical trials, and clinical medication studies. The review is based on literature retrieval from the PubMed and Web of Science databases between 2018 and 2024.

EXPERT OPINION: As technologies advance rapidly, NGS enhances accuracy and generates vast datasets. These datasets are extensively integrated with other heterogeneous data in systems biology and are mined using machine learning to extract significant insights, thereby driving progress in drug discovery.

PMID:40099494 | DOI:10.1080/17460441.2025.2481262

Front Immunol. 2025 Mar 3;16:1511902. doi: 10.3389/fimmu.2025.1511902. eCollection 2025.

ABSTRACT

INTRODUCTION: Alterations of the adaptive immune system have been shown to impact bone healing and may result in impaired healing in some patients. Apart from T cells, B cells are the key drivers of adaptive immunity. Therefore, their role in age-associated impairments of bone healing might be essential to understand delays during the healing process. B cells are essential for bone formation, and their dysfunction has been associated with aging or autoimmune diseases. But whether age-associated changes in B cell phenotypes are involved in bone regeneration is unknown.

METHODS: Here, we aimed to characterize the role of immune aging in B cell phenotypes during the early inflammatory phase of bone healing. By comparing non-immune experienced with young and immune experienced mice we aimed to analyze the effect of gained immune experience on B cells. Our single cell proteo-genomics analysis quantified thousands of transcriptomes of cells that were isolated from post osteotomy hematoma and the proximal and distal bone marrow cavities, and enabled us to evaluate cell proportion, differential gene expression and cell trajectories.

RESULTS: While the B cell proportion in young and non-immune experienced animals did not significantly change from 2 to 5 days post osteotomy in the hematoma, we found a significant decrease of the B cell proportion in the immune experienced mice, which was accompanied by the decreased expression of B cell specific genes, suggesting a specific response in immune experienced animals. Furthermore, we detected the most extensive B cell differentiation block in immune-experienced mice compared to non-immune experienced and young animals, predominantly in the transition from immature to mature B cells.

DISCUSSION: Our results suggest that the pronounced impairment of B cell production found in immune experienced animals plays an important role in the initial phase leading to delayed bone healing. Therefore, novel therapeutic approaches may be able target the B cell differentiation defect to retain B cell functionality even in the immune experienced setting, which is prone to delayed healing.

PMID:40098964 | PMC:PMC11911212 | DOI:10.3389/fimmu.2025.1511902

Expert Opin Drug Discov. 2025 Mar 17. doi: 10.1080/17460441.2025.2481259. Online ahead of print.

ABSTRACT

INTRODUCTION: The landscape of early drug discovery is rapidly evolving, fueled by significant advancements in artificial intelligence (AI) and machine learning (ML), which are transforming the way drugs are discovered. As traditional drug discovery faces growing challenges in terms of time, cost, and efficacy, there is a pressing need to integrate these emerging technologies to enhance the discovery process.

AREAS COVERED: In this perspective, the authors explore the role of AI and ML in modern early drug discovery and discuss their application in drug target identification, compound screening, and biomarker discovery. This article is based on a thorough literature search using the PubMed database to identify relevant studies that highlight the use of AI/ML models in computational chemistry, systems biology, and data-driven approaches to drug development. Emphasis is placed on how these technologies address key challenges such as data integration, predictive performance, and cost-efficiency in the drug discovery pipeline.

EXPERT OPINION: AI and ML have the potential to revolutionize early drug discovery by improving the accuracy and speed of identifying viable drug candidates. However, successful integration of these technologies requires overcoming challenges related to data quality, model interpretability, and the need for interdisciplinary collaboration.

PMID:40098331 | DOI:10.1080/17460441.2025.2481259

BMC Plant Biol. 2025 Mar 18;25(1):345. doi: 10.1186/s12870-025-06322-2.

ABSTRACT

BACKGROUND: The quality of groundnut produce is adversely impacted due to aflatoxin contamination by the fungus Aspergillus flavus. Although the transcriptomic control is not fully understood, the interaction between long non-coding RNAs and microRNAs in regulating A. flavus and aflatoxin contamination remains unclear. This study was carried out to identify microRNAs (miRNAs) to enhance the understanding of in vitro seed colonization (IVSC) resistance mechanism in groundnut.

RESULT: In this study, resistant (J 11) and susceptible (JL 24) varieties of groundnut were treated with toxigenic A. flavus (strain AF-11-4), and total RNA was extracted at 1 day after inoculation (1 DAI), 2 DAI, 3 DAI and 7 DAI. Seeds of JL 24 showed higher mycelial growth than J 11 at successive days after inoculation. A total of 208 known miRNAs belonging to 36 miRNA families, with length varying from 20-24 nucleotides, were identified, along with 27 novel miRNAs, with length varying from 20-22 nucleotides. Using psRNATarget server, 952 targets were identified for all the miRNAs. The targeted genes function as disease resistant proteins encoding, auxin responsive proteins, squamosa promoter binding like proteins, transcription factors, pentatricopeptide repeat-containing proteins and growth regulating factors. Through differential expression analysis, seven miRNAs (aly-miR156d-3p, csi-miR1515a, gma-miR396e, mtr-miR2118, novo-miR-n27, ptc-miR482d-3p and ppe-miR396a) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in J 11, whereas ten miRNAs (csi-miR159a-5p, csi-miR164a-3p, novo-miR-n17, novo-miR-n2, osa-miR162b, mtr-miR2118, ptc-miR482d-3p, ptc-miR167f-3p, stu-miR319-3p and zma-miR396b-3p) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in JL 24. Two miRNAs, ptc-miR482d-3p and mtr-miR2118, showed contrasting expression at different time intervals between J 11 and JL 24. These two miRNAs were found to target those genes with NBS-LRR function, making them potential candidates for marker development in groundnut breeding programs aimed at enhancing resistance against A. flavus infection.

CONCLUSION: This study enhances our understanding of the involvement of two miRNAs namely, ptc-miR482d-3p and mtr-miR2118, along with their NBS-LRR targets, in conferring resistance against A. flavus-induced aflatoxin contamination in groundnut under in vitro conditions.

PMID:40098099 | DOI:10.1186/s12870-025-06322-2

Nat Genet. 2025 Mar 17. doi: 10.1038/s41588-025-02121-5. Online ahead of print.

ABSTRACT

Genetic mutation and drift, coupled with natural and human-mediated selection and migration, have produced a wide variety of genotypes and phenotypes in farmed animals. We here introduce the Farm Animal Genotype-Tissue Expression (FarmGTEx) Project, which aims to elucidate the genetic determinants of gene expression across 16 terrestrial and aquatic domestic species under diverse biological and environmental contexts. For each species, we aim to collect multiomics data, particularly genomics and transcriptomics, from 50 tissues of 1,000 healthy adults and 200 additional animals representing a specific context. This Perspective provides an overview of the priorities of FarmGTEx and advocates for coordinated strategies of data analysis and resource-sharing initiatives. FarmGTEx aims to serve as a platform for investigating context-specific regulatory effects, which will deepen our understanding of molecular mechanisms underlying complex phenotypes. The knowledge and insights provided by FarmGTEx will contribute to improving sustainable agriculture-based food systems, comparative biology and eventual human biomedicine.

PMID:40097783 | DOI:10.1038/s41588-025-02121-5

Breast Cancer Res Treat. 2025 Mar 17. doi: 10.1007/s10549-025-07666-x. Online ahead of print.

ABSTRACT

PURPOSE: Endocrine therapy resistance is the major challenge of managing patients with estrogen receptor positive (ER+) breast cancer. We previously reported frequent overexpression of FGFR4 in endocrine-resistant cell lines and breast cancers that recurred and metastasized following endocrine therapy, suggesting FGFR4 as a potential driver of endocrine resistance. In this study, we investigated the role of FGFR4 in mediating endocrine resistance and explored the therapeutic potential of targeting FGFR4 in advanced breast cancer.

METHODS: A gene expression signature of FGFR4 activity was examined in ER+breast cancer pre- and post-neoadjuvant endocrine therapy and the association between FGFR4 expression and patient survival was examined. A correlation analysis was used to uncover potential regulators of FGFR4 overexpression. To investigate if FGFR4 is necessary to drive endocrine resistance, we tested response to FGFR4 inhibition in long-term estrogen-deprived (LTED) cells and their paired parental cells. Doxycycline inducible FGFR4 overexpression and knockdown cell models were generated to examine if FGFR4 was sufficient to confer endocrine resistance. Finally, we examined response to FGFR4 monotherapy or combination therapy with fulvestrant in breast cancer cell lines to explore the potential of FGFR4 targeted therapy for advanced breast cancer and assessed the importance of PAM50 subtype in response to FGFR4 inhibition.

RESULTS: A FGFR4 activity gene signature was significantly upregulated post-neoadjuvant aromatase inhibitor treatment, and high FGFR4 expression predicted poorer survival in patients with ER+breast cancer. Gene expression association analysis using TCGA, METABRIC, and SCAN-B datasets uncovered ER as the most significant gene negatively correlated with FGFR4 expression. ER negatively regulates FGFR4 expression at both the mRNA and protein level across multiple ER+breast cancer cell lines. Despite robust overexpression of FGFR4, LTED cells did not show enhanced responses to FGFR4 inhibition compared to parental cells. Similarly, FGFR4 overexpression and knockdown did not substantially alter response to endocrine treatment in ER+cell lines, nor did FGFR4 and fulvestrant combination treatment show synergistic effects. The HER2-like subtype of breast cancer showed elevated expression of FGFR4 and an increased response to FGFR4 inhibition relative to other breast cancer subtypes.

CONCLUSIONS: Despite ER-mediated upregulation of FGFR4 post-endocrine therapy, our study does not support a general role of FGFR4 in mediating endocrine resistance in ER+breast cancer. The significant upregulation of FGFR4 expression in treatment-resistant clinical samples and models following endocrine therapy does not necessarily establish a causal link between the gene and treatment response. Our data suggest that specific genomic backgrounds such as HER2 expression may be required for FGFR4 function in breast cancer and should be further explored.

PMID:40097769 | DOI:10.1007/s10549-025-07666-x

Nat Struct Mol Biol. 2025 Mar 17. doi: 10.1038/s41594-025-01517-5. Online ahead of print.

ABSTRACT

Deubiquitylases (DUBs) are crucial in cell signaling and are often regulated by interactions within protein complexes. The BRCC36 isopeptidase complex (BRISC) regulates inflammatory signaling by cleaving K63-linked polyubiquitin chains on type I interferon receptors (IFNAR1). As a Zn2+-dependent JAMM/MPN (JAB1, MOV34, MPR1, Pad1 N-terminal) DUB, BRCC36 is challenging to target with selective inhibitors. Here, we discover first-in-class inhibitors, termed BRISC molecular glues (BLUEs), which stabilize a 16-subunit human BRISC dimer in an autoinhibited conformation, blocking active sites and interactions with the targeting subunit, serine hydroxymethyltransferase 2. This unique mode of action results in selective inhibition of BRISC over related complexes with the same catalytic subunit, splice variants and other JAMM/MPN DUBs. BLUE treatment reduced interferon-stimulated gene expression in cells containing wild-type BRISC and this effect was abolished when using structure-guided, inhibitor-resistant BRISC mutants. Additionally, BLUEs increase IFNAR1 ubiquitylation and decrease IFNAR1 surface levels, offering a potential strategy to mitigate type I interferon-mediated diseases. Our approach also provides a template for designing selective inhibitors of large protein complexes by promoting rather than blocking protein-protein interactions.

PMID:40097626 | DOI:10.1038/s41594-025-01517-5

NPJ Syst Biol Appl. 2025 Mar 17;11(1):27. doi: 10.1038/s41540-025-00501-5.

ABSTRACT

A molecular systems architecture is presented for the neuromuscular junction (NMJ) in order to provide a framework for organizing complexity of biomolecular interactions in amyotrophic lateral sclerosis (ALS) using a systematic literature review process. ALS is a fatal motor neuron disease characterized by progressive degeneration of the upper and lower motor neurons that supply voluntary muscles. The neuromuscular junction contains cells such as upper and lower motor neurons, skeletal muscle cells, astrocytes, microglia, Schwann cells, and endothelial cells, which are implicated in pathogenesis of ALS. This molecular systems architecture provides a multi-layered understanding of the intra- and inter-cellular interactions in the ALS neuromuscular junction microenvironment, and may be utilized for target identification, discovery of single and combination therapeutics, and clinical strategies to treat ALS.

PMID:40097438 | DOI:10.1038/s41540-025-00501-5

Sci Total Environ. 2025 Mar 15;972:179010. doi: 10.1016/j.scitotenv.2025.179010. Online ahead of print.

ABSTRACT

Sewage contamination of freshwater occurs in the form of raw waste or as effluent from wastewater treatment plants (WWTP's). While raw waste (animal and human) and under-functioning WWTP's can introduce live enteric bacteria to freshwater systems, most WWTP's, even when operating correctly, do not remove bacterial genetic material from treated waste, resulting in the addition of bacterial DNA, including antibiotic resistance genes, into water columns and sediment of freshwater systems. In freshwater systems with both raw and treated waste inputs, then, there will be increased interaction between live sewage-associated bacteria (untreated sewage) and DNA contamination (from both untreated and treated wastewater effluent). To evaluate this understudied interaction between DNA and bacterial contamination in the freshwater environment, we conducted a three-month field-based study of sewage-associated bacteria and genetic material in water and sediment in a freshwater tributary of the Hudson River (NY, USA) that supplies drinking water and receives treated and untreated wastewater discharges from several municipalities. Using both DNA and culture-based bacterial analyses, we found that both treated and untreated sewage influences water and sediment bacterial communities in this tributary, and water-sediment exchanges of enteric bacteria and genetic material. Our results also indicated that the treated sewage effluent on this waterway serves as a concentrated source of intI1 (antibiotic resistance) genes, which appear to collect in the sediments below the outfall along with fecal indicator bacteria. Our work also captured the environmental impact of a large rain event that perturbed bacterial populations in sediment and water matrices, independently from the outflow. This study suggests that large precipitation events are an important cause of bacterial and DNA contamination for freshwater tributaries, with runoff from the surrounding environment being an important factor.

PMID:40096758 | DOI:10.1016/j.scitotenv.2025.179010

Aging (Albany NY). 2025 Mar 12;17. doi: 10.18632/aging.206220. Online ahead of print.

ABSTRACT

The dynamic nature of epigenetic modifications has been leveraged to construct epigenetic clocks that accurately predict an individual's age based on DNA methylation levels. Here we explore whether the accumulation of epimutations, which can be quantified by Shannon's entropy, changes reproducibly with age. Using targeted bisulfite sequencing, we analyzed the associations between age, entropy, and methylation levels in human buccal swab samples. We find that epigenetic clocks based on the entropy of methylation states predict chronological age with similar accuracy as common approaches that are based on methylation levels of individual cytosines. Our approach suggests that across many genomic loci, methylation entropy changes reproducibly with age.

PMID:40096548 | DOI:10.18632/aging.206220

Plant Cell. 2025 Mar 17:koaf055. doi: 10.1093/plcell/koaf055. Online ahead of print.

ABSTRACT

Photosystem (PS) II assembly requires auxiliary factors, including Psb28. Although the absence of Psb28 in cyanobacteria has little effect on PSII assembly, we show here that the Chlamydomonas (Chlamydomonas reinhardtii) psb28 null mutant is severely impaired in PSII assembly, showing drastically reduced PSII supercomplexes, dimers and monomers, while overaccumulating early PSII assembly intermediates reaction center II (RCII), CP43mod and D1mod. The mutant had less PSI and more cytochrome b6f complex, its thylakoids were organized mainly as monolayers and it had a distorted chloroplast morphology. Complexome profiling of the psb28 mutant revealed that THYLAKOID ENRICHED FRACTION 5 (TEF5), the homolog of Arabidopsis (Arabidopsis thaliana) PHOTOSYSTEM B PROTEIN 33 (PSB33)/LIGHT HARVESTING-LIKE 8 (LIL8), co-migrated particularly with RCII. TEF5 also interacted with PSI. A Chlamydomonas tef5 null mutant was severely impaired in PSII assembly and overaccumulated RCII and CP43mod. RC47 was not detectable in the light-grown tef5 mutant. Our data suggest a possible role for TEF5 in RCII photoprotection or maturation. Both the psb28 and tef5 mutants exhibited decreased synthesis of CP47 and PsbH, suggesting negative feedback regulation possibly exerted by the accumulation of RCII and/or CP43mod in both mutants. The strong effects of missing auxiliary factors on PSII assembly in Chlamydomonas suggest a more effective protein quality control system in this alga than in land plants and cyanobacteria.

PMID:40096524 | DOI:10.1093/plcell/koaf055

Elife. 2025 Mar 17;14:RP103877. doi: 10.7554/eLife.103877.

ABSTRACT

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (1) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct Escherichia coli promoters and (2) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

PMID:40095799 | DOI:10.7554/eLife.103877

Plant Biol (Stuttg). 2025 Mar 17. doi: 10.1111/plb.70012. Online ahead of print.

ABSTRACT

Iron (Fe) is essential for plants and humans, with over 2 billion people suffering deficiency disorders because most plant foods, including cereals, are low in Fe. Durum wheat, a staple crop in Mediterranean regions, is facing increased droughts, which reduce plant yield and ability to acquire and use Fe. Therefore, understanding mechanisms underlying Fe acquisition and accumulation in durum wheat under drought is essential for both agronomic and nutritional purposes. Here, a durum wheat (Triticum turgidum subsp. durum) genotype with a purple grain pericarp was grown hydroponically under adequate (80 μM) or limited (10 μM) Fe, with or without water stress (induced with 10% PEG 6000) for 6 days. Fe accumulation decreased under Fe deficiency and drought, with the highest phytosiderophore (PS) release in Fe-deficient plants. Interestingly, despite adequate Fe availability, drought inhibited Fe accumulation in roots. This response was accompanied by increased release of PS from roots, although the increase was less than that observed with single or combined Fe deficiency. Both TdIRT1 and TdYS15 were upregulated by Fe deficiency but downregulated by drought and the combined stress. Drought stress and Fe deficiency led to increased ABA production, being 250-fold higher with respect to controls. TdIRT1 downregulation in plants exposed to the combined stress suggests a trade-off between water and Fe stress responses. Our findings demonstrate that the response to combined stress differs from, and is rarely additive to, the response to a single stressor, reinforcing the complexity of plant adaptation to combined environmental stresses.

PMID:40095748 | DOI:10.1111/plb.70012

FASEB J. 2025 Mar 31;39(6):e70419. doi: 10.1096/fj.202401661RRR.

ABSTRACT

Zinc finger-containing ubiquitin peptidase 1 (ZUP1) is a protein characterized by four N-terminal zinc finger domains and a C-terminal deubiquitinase (DUB) domain. While it is associated with the DNA damage response, the role of ZUP1 in innate immunity remains unclear. Here, we identify ZUP1 as a crucial component of the mitochondrial antiviral signaling (MAVS) complex, essential for host antiviral defense. We show that viral infection significantly upregulates ZUP1 expression, and mice lacking ZUP1 exhibit impaired type I interferon (IFN) production and increased susceptibility to viral infection, as evidenced by higher mortality rates. This underscores the protective role of ZUP1 in host immunity. Mechanistically, ZUP1 binds to MAVS through its C-terminal domain independently of DUB activity. Instead, ZUP1 utilizes its zinc finger domains, particularly the third zinc finger, to directly bind viral RNA. This interaction enhances the association of ZUP1 with MAVS and promotes its aggregation on mitochondria during viral infection. ZUP1 also interacts with TBK1 and NEMO within the MAVS complex, facilitating IRF3 activation and type I IFN production. These findings establish ZUP1 as a zinc finger-containing regulator that amplifies MAVS-dependent antiviral immunity, linking viral RNA recognition to downstream signaling and highlighting potential targets for therapeutic intervention against viral infections.

PMID:40095368 | DOI:10.1096/fj.202401661RRR

Planta. 2025 Mar 17;261(4):92. doi: 10.1007/s00425-025-04666-5.

ABSTRACT

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

PMID:40095140 | DOI:10.1007/s00425-025-04666-5

Plants (Basel). 2025 Mar 1;14(5):761. doi: 10.3390/plants14050761.

ABSTRACT

The regulatory mechanisms underlying embryogenic callus (EC) formation in polyploid bananas remain unexplored, posing challenges for genetic transformation and biotechnological applications. Here, we conducted transcriptome sequencing on cultured explants, non-embryogenic callus, EC, and browning callus in the ABB cultivar 'MJ' (Musa spp. cv. Bengal). Our analysis of differentially expressed genes (DEGs) revealed significant enrichment in plant hormones, MAPK, and zeatin biosynthesis pathways. Notably, most genes in the MJ variety exhibited balanced expression of the A and B alleles, but A-specific allele expression was dominant in the key signaling pathways, whereas B-specific allele expression was very rare during EC induction. In the auxin signaling pathway, six A-specific MJARF genes were markedly downregulated, underscoring their critical roles in the negative regulation of callus formation. Additionally, six A-specific MJEIN3 alleles were found to play negative regulatory roles in ethylene signaling during EC development. We also identified phenylpropanoids responsible for enzymatic browning. Furthermore, the expression patterns of transcription factors in bananas exhibited specific expression modes, highlighting the unique mechanisms of callus formation. This study enhanced our understanding of the regulatory roles of these alleles in EC induction and offers new insights into the utilization of alleles to improve the efficiency of somatic embryogenesis in bananas.

PMID:40094726 | DOI:10.3390/plants14050761