J Biotechnol. 2025 May 6:S0168-1656(25)00109-9. doi: 10.1016/j.jbiotec.2025.04.024. Online ahead of print.

ABSTRACT

The single-step biotransformation of the natural compound piperine into a known dual inhibitor of monoamine oxidase B (MAO-B) and catechol-O-methyltransferase (COMT), was achieved by cytochrome P450 BM3 wild-type and the D251G/Q307H double mutant. This compound is used for research in neurodegenerative disorders, such as Parkinson's disease, and its value in the market is ~14,000 €/g. Currently, it is produced by chemical synthesis requiring incubation of piperine with boron tribromide (BBr3) in dichloromethane with yield of product not exceeding 55% and using tedious and long procedure for its production and isolation. The P450 D251G/Q307H double mutant exhibited a 3-fold increase in catalytic efficiency compared to the wild-type enzyme, achieving high conversion (51.6% of conversion in 15minutes) under mild, environmentally friendly conditions. The yield of production was 0.01mg of the inhibitor in 1mL of reaction in 15minutes at 28°C using the purified enzyme. Moreover, biological assays demonstrated that the resulting compound has a novel and stronger antioxidant and antimicrobial activities, respectively, when compared to piperine. The data further demonstrates the broader potential of engineered enzymes as versatile and sustainable tools in industrial biotechnology, offering an efficient platform for the modification of natural compounds to produce bioactive molecules.

PMID:40339650 | DOI:10.1016/j.jbiotec.2025.04.024

Cell. 2025 May 5:S0092-8674(25)00353-8. doi: 10.1016/j.cell.2025.03.035. Online ahead of print.

ABSTRACT

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses.

PMID:40339581 | DOI:10.1016/j.cell.2025.03.035

Cell Syst. 2025 May 5:101292. doi: 10.1016/j.cels.2025.101292. Online ahead of print.

ABSTRACT

The arginine dihydrolase pathway (arc operon) provides a metabolic niche by transforming arginine into metabolic byproducts. We investigate the role of the arc operon in probiotic Escherichia coli Nissle 1917 on human gut community assembly and health-relevant metabolite profiles. By stabilizing environmental pH, the arc operon reduces variability in community composition in response to pH perturbations and frequently enhances butyrate production in synthetic communities. We use a tailored machine learning model for microbiomes to predict community assembly in response to variation in initial media pH and arc operon activity. This model uncovers the pH- and arc operon-dependent interactions shaping community assembly. Human gut species display altered colonization dynamics in response to the arc operon in the murine gut. In sum, our framework to quantify the contribution of a specific pathway to microbial community assembly and metabolite production can reveal new engineering strategies. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:40339579 | DOI:10.1016/j.cels.2025.101292

Dev Cell. 2025 May 2:S1534-5807(25)00250-3. doi: 10.1016/j.devcel.2025.04.012. Online ahead of print.

ABSTRACT

The lymphatic vasculature comprises lymphatic capillaries and collecting vessels. To support lymphatic development, lymphatic endothelial cells (LECs) utilize nutrients to fuel lymphangiogenic processes. Meanwhile, LECs maintain constant prospero homeobox 1 (PROX1) expression critical for lymphatic specification. However, molecular mechanisms orchestrating nutrient metabolism while sustaining PROX1 levels in LECs remain unclear. Here, we show that loss of RAPTOR, an indispensable mechanistic target of rapamycin complex 1 (mTORC1) component, downregulates PROX1 and impairs lymphatic capillary growth and differentiation of collecting lymphatics in mice. Mechanistically, mTORC1 inhibition in mouse and human LECs causes Myc reduction, which decreases hexokinase 2 (HK2) and glutaminase (GLS), inhibiting glycolysis and glutaminolysis. Myc or HK2/GLS ablation impedes lymphatic capillary and collecting vessel formation. Interestingly, mTORC1 regulation of PROX1 is independent of Myc-HK2/GLS signaling. Moreover, genetic interaction analysis indicates that Myc and PROX1 play crucial roles in mTORC1-regulated lymphatic development. Collectively, our findings identify mTORC1 as a key regulator of metabolic programs and PROX1 expression during lymphangiogenesis.

PMID:40339577 | DOI:10.1016/j.devcel.2025.04.012

Proc Natl Acad Sci U S A. 2025 May 13;122(19):e2502368122. doi: 10.1073/pnas.2502368122. Epub 2025 May 8.

ABSTRACT

Single-cell organisms and various cell types use a range of motility modes when following a chemical gradient, but it is unclear which mode is best suited for different gradients. Here, we model directional decision-making in chemotactic amoeboid cells as a stimulus-dependent actin recruitment contest. Pseudopods extending from the cell body compete for a finite actin pool to push the cell in their direction until one pseudopod wins and determines the direction of movement. Our minimal model provides a quantitative understanding of the strategies cells use to reach the physical limit of accurate chemotaxis, aligning with data without explicit gradient sensing or cellular memory for persistence. To generalize our model, we employ reinforcement learning optimization to study the effect of pseudopod suppression, a simple but effective cellular algorithm by which cells can suppress possible directions of movement. Different pseudopod-based chemotaxis strategies emerge naturally depending on the environment and its dynamics. For instance, in static gradients, cells can react faster at the cost of pseudopod accuracy, which is particularly useful in noisy, shallow gradients where it paradoxically increases chemotactic accuracy. In contrast, in dynamics gradients, cells form de novo pseudopods. Overall, our work demonstrates mechanical intelligence for high chemotaxis performance with minimal cellular regulation.

PMID:40339116 | DOI:10.1073/pnas.2502368122

Curr Med Chem. 2025 May 7. doi: 10.2174/0109298673367163250417065816. Online ahead of print.

ABSTRACT

BACKGROUND: Developing new COVID-19 antivirals requires understanding viral proteins, oxidative stress, and drug repositioning. Safety assessments of organochalcogen molecules derived from AZT in Caenorhabditis elegans offer promising prospects for new treatments.

OBJECTIVE: In this work, we evaluated the safety and antioxidant effect of eight organochalcogen AZT-derivatives using the free-living nematode C. elegans through chronic exposure [48h]. In addition, we used in silico computational modelling analyses to predict protein targets for these compounds.

METHODS: This study used survival, litter size, brood size as toxicological and safety parameters, subcellular localization of DAF-16, expression of SOD-3 and GST-4, and ROS levels to evaluate the antioxidant effects and target prediction by similarity set approach [SEA], protein-protein interaction [PPI] network analysis, and comparative phylogenetic analysis to predict protein targets for these compounds.

RESULTS: The molecules were safe at concentrations of 1-500 μM. AZT, R3a, and R3f promoted DAF-16 nuclear translocation without affecting SOD-3 levels. R3f reduced GST-4 levels, while R3a increased ROS levels. In silico analyses identified 16 human protein targets of AZT and its derivatives, linked to nucleotide metabolism, DNA replication, and anti-inflammatory pathways, showing high homology to C. elegans.

CONCLUSION: We hypothesize that Se and Te atom insertion may alter pharmacological properties by modulating DAF-16, GST-4, and ROS-related pathways. in silico data suggest these derivatives are promising for antiviral activity, targeting nucleotide metabolism and DNA replication while also potentially modulating the anti-inflammatory response, an appealing feature for COVID-19 treatment.

PMID:40337965 | DOI:10.2174/0109298673367163250417065816

Nucleic Acids Res. 2025 May 8:gkaf399. doi: 10.1093/nar/gkaf399. Online ahead of print.

ABSTRACT

Enzymes with buried active sites utilize molecular tunnels to exchange substrates, products, and solvent molecules with the surface. These transport mechanisms are crucial for protein function and influence various properties. As proteins are inherently dynamic, their tunnels also vary structurally. Understanding these dynamics is essential for elucidating structure-function relationships, drug discovery, and bioengineering. Caver Web 2.0 is a user-friendly web server that retains all Caver Web 1.0 functionalities while introducing key improvements: (i) generation of dynamic ensembles via automated molecular dynamics with YASARA, (ii) analysis of dynamic tunnels with CAVER 3.0, (iii) prediction of ligand trajectories in multiple snapshots with CaverDock 1.2, and (iv) customizable ligand libraries for virtual screening. Users can assess protein flexibility, identify and characterize tunnels, and predict ligand trajectories and energy profiles in both static and dynamic structures. Additionally, the platform supports virtual screening with FDA/EMA-approved drugs and user-defined datasets. Caver Web 2.0 is a versatile tool for biological research, protein engineering, and drug discovery, aiding the identification of strong inhibitors or new substrates to bind to the active sites or tunnels, and supporting drug repurposing efforts. The server is freely accessible at https://loschmidt.chemi.muni.cz/caverweb.

PMID:40337920 | DOI:10.1093/nar/gkaf399

mSphere. 2025 May 8:e0108124. doi: 10.1128/msphere.01081-24. Online ahead of print.

ABSTRACT

Biofilms are an important colonization mechanism employed by several microbial species to better establish themselves and monopolize the acquisition of resources across different environs. Some bacteria have evolved specialized metabolites that, when secreted, disrupt the formation and stability of biofilms generated by competing heterospecies, providing the producing organism with an ecological advantage. Soil-derived species are probable candidates for the identification of such compounds, given the intense level of competition that occurs within the terrestrial ecosystem. The MS14 strain of Burkholderia contaminans isolated from soil in Mississippi has previously been shown to produce antimicrobial compounds like occidiofungin and ornibactin. In this report, we demonstrate that this strain also produces 4-hydroxy-3-methyl-2-alkenylquinoline (HMAQ-7), an alkaloid-based metabolite structurally similar to others produced by Burkholderia. HMAQ-7 was isolated and purified in sufficient quantities to enable the elucidation of its covalent structure and the evaluation of its biological effects. The compound was found to possess a unique ability to inhibit biofilm biosynthesis in several species, including opportunistic pathogens like Staphylococcus haemolyticus and within saliva-derived multispecies biofilms. HMAQ-7 also demonstrated an ability to modulate additional cellular behaviors in Bacillus subtilis, including motility and sporulation, suggesting that this molecule is important to the interspecies dynamics present across many diverse microenvironments.IMPORTANCEThe present study furthers our understanding of the structural complexity and the biological functions of the 2-alkyl-4(1H)-quinolone metabolites produced by Burkholderia spp. Low micromolar concentrations of HMAQ-7' induced observable bacterial growth morphology differences. The antibiofilm properties of the HMAQ-7' characterized in this study will promote future investigations into possible biological and applied roles. The ability to alter biofilm formation using HMAQ-7' may facilitate Burkholderia spp. colonization in a multitude of environments, that is, aquatic, soil, and possibly during infection. HMAQ may subvert competition by potential competitor species in natural environments of Burkholderia spp. and possibly lung infections of cystic fibrosis patients.

PMID:40338090 | DOI:10.1128/msphere.01081-24

J Phys Chem A. 2025 May 8. doi: 10.1021/acs.jpca.5c01243. Online ahead of print.

ABSTRACT

Multiresonance thermally activated delayed fluorescence (MR-TADF) emitters are crucial for the next generation of electroluminescent devices due to their high efficiency and narrowband emission. In this study, we developed a simple molecular design for MR-TADF materials based on a π-extended DABNA core decorated with four different framework types (carbazole (X = none), acridine (X = C(Me)2), phenoxazine (X = O), and phenothiazine (X = S)) and further modified with 18 different annulated systems. The optoelectronic properties of these compounds were modeled using density functional theory. Based on quantum chemical calculations, an accelerated search tool for MR-TADF emitters was developed using deep learning methods, enabling the prediction of energy values approximating experimental results.

PMID:40338523 | DOI:10.1021/acs.jpca.5c01243

Med Biol Eng Comput. 2025 May 8. doi: 10.1007/s11517-025-03362-6. Online ahead of print.

ABSTRACT

Brain signals carry cognitive information that can be relevant in downstream tasks, but what about eye-gaze? Although this can be estimated with eye-trackers, it can be very convenient in practice to do it without extra equipment. We consider the challenging tasks of fixation prediction and gaze estimation from electroencephalography (EEG) using deep learning models. We argue that there are three critical design criteria when designing neural architectures for EEG: (1) the spatial and temporal dimensions of the data, (2) the local vs global nature of the data processing, and (3) the overall structure and order with which the steps (1) and (2) are orchestrated. We propose two model architectures, based on Transformers and LSTMs, with different variants in this large design space, and compare them with recent state-of-the-art (SOTA) approaches under two constraints: reduced EEG signal length and reduced set of EEG channels. Our Transformer-based model outperforms the LSTM-only model, but it turns out to be more sensitive with short signal lengths and with less number of channels. Interestingly, our results are similar or slightly better than SOTA, and the models are trained from scratch (i.e., without pre-training or fine-tuning). Our findings provide useful insights for advancing in eye-from-EEG tasks.

PMID:40338479 | DOI:10.1007/s11517-025-03362-6

J Comput Assist Tomogr. 2025 May 5. doi: 10.1097/RCT.0000000000001765. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the effectiveness of the new-generation snapshot freeze (SSF2) algorithm combined with Deep Learning Image Reconstruction (DLIR) in improving the image quality of coronary artery calcifications (CAC) and their quantification.

METHODS: Coronary artery calcification score (CACS) scans were performed on 69 patients using ECG-triggered noncontrast CT. Four groups of images were reconstructed with SSF2 or without (STD), combined with ASIR-V (Adaptive Statistical Iterative Reconstruction-V) and DLIR: STDASIR-V, STDDLIR, SSF2ASIR-V, and SSF2DLIR. CAC image quality was compared, and inter-observer consistency was evaluated among reconstruction groups. CACS, including the Agatston score (AS), volume score (VS), mass score (MS), and the risk stratification based on AS among groups, were compared.

RESULTS: The consistencies of the inter-observer image quality scores were excellent or good (kappa=0.705 to 0.837). SSF2ASIR-V and SSF2DLIR had significantly higher scores than STDASIR-V and STDDLIR in reducing motion artifacts of calcified plaques (P<0.05), while no significant differences between SSF2ASIR-V and SSF2DLIR, or between STDASIR-V and STDDLIR (P>0.05). There was no significant difference in CT values of vessels, subcutaneous fat, and muscle in CAC images, but the noises of SSF2ASIR-V and STDASIR-V images were significantly higher than those of SSF2DLIR and STDDLIR images (P>0.05). STDASIR-V had the highest CACS values, while SSF2DLIR had the lowest. Using AS in STDASIR-V as the reference, 9 patients (13.04%) in SSF2DLIR and 7 patients (10.14%) in SSF2ASIR-V had a risk stratification reduced, while no change in STDDLIR.

CONCLUSIONS: SSF2 and DLIR significantly reduce motion artifacts and image noise in non-contrast CACS CT, respectively. SSF2 reduces CACS values and risk stratification.

PMID:40338070 | DOI:10.1097/RCT.0000000000001765

J Comput Assist Tomogr. 2025 May 6. doi: 10.1097/RCT.0000000000001764. Online ahead of print.

ABSTRACT

OBJECTIVE: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving prognosis. This study aimed to develop and validate a model (DLRR) that incorporates deep learning radiomics and traditional radiomics features to predict ER following curative ablation for HCC.

METHODS: We retrospectively analysed the data of 288 eligible patients from 3 hospitals-1 primary cohort (center 1, n=222) and 2 external test cohorts (center 2, n=32 and center 3, n=34)-from April 2008 to March 2022. 3D ResNet-18 and PyRadiomics were applied to extract features from contrast-enhanced computed tomography (CECT) images. The 3-step (ICC-LASSO-RFE) method was used for feature selection, and 6 machine learning methods were used to construct models. Performance was compared through the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. Calibration and clinical applicability were assessed through calibration curves and decision curve analysis (DCA), respectively. Kaplan-Meier (K-M) curves were generated to stratify patients based on progression-free survival (PFS) and overall survival (OS).

RESULTS: The DLRR model had the best performance, with AUCs of 0.981, 0.910, and 0.851 in the training, internal validation, and external validation sets, respectively. In addition, the calibration curve and DCA curve revealed that the DLRR model had good calibration ability and clinical applicability. The K-M curve indicated that the DLRR model provided risk stratification for progression-free survival (PFS) and overall survival (OS) in HCC patients.

CONCLUSIONS: The DLRR model noninvasively and efficiently predicts ER after curative ablation in HCC patients, which helps to categorize the risk in patients to formulate precise diagnosis and treatment plans and management strategies for patients and to improve the prognosis.

PMID:40338065 | DOI:10.1097/RCT.0000000000001764

Clin Exp Rheumatol. 2025 May;43(5):815-821. doi: 10.55563/clinexprheumatol/oamfed. Epub 2025 May 8.

ABSTRACT

Artificial intelligence (AI) has its roots in the history of philosophy and of applied mathematics of the 17th, 18th and 19th centuries. Throughout the 20th century, significant advancements in mathematics and computer science laid the groundwork for modern AI, culminating in the establishment of the field as a formal discipline during the Dartmouth Conference in 1956.This pivotal event brought together leading researchers who envisioned creating machines capable of simulating human intelligence, setting the stage for decades of research and innovation in the field. The development of early AI systems focused on problem-solving and symbolic reasoning, leading to the creation of programmes that could play games like chess and solve mathematical equations, which show-cased the potential of machines to perform tasks previously thought to require human intellect.As these foundational systems evolved, researchers began to explore more complex algorithms and learning models, paving the way for advancements in machine learning and neural networks that would eventually revolutionise AI applications across various fields among which medicine. The growth of big data and increased computational power further accelerated these advancements, enabling machines to analyse vast amounts of health information and learn from patterns at unprecedented speeds.The revolution of deep learning and soon after large language models has enabled machines to achieve remarkable feats, such as image and speech recognition, natural language processing, and even creative tasks like art generation, pushing the boundaries of what was once thought possible. As organisations grapple with these challenges, there is growing emphasis on developing frameworks that ensure responsible AI deployment while maximising its potential benefits for human health.

PMID:40338059 | DOI:10.55563/clinexprheumatol/oamfed

Nucleic Acids Res. 2025 May 8:gkaf403. doi: 10.1093/nar/gkaf403. Online ahead of print.

ABSTRACT

Predicting functional binding sites in proteins is crucial for understanding protein-protein interactions (PPIs) and identifying drug targets. While various computational approaches exist, many fail to assess PPI ligandability, which often involves conformational changes. We introduce InDeepNet, a web-based platform integrating InDeep, a deep-learning model for binding site prediction, with InDeepHolo, which evaluates a site's propensity to adopt a ligand-bound (holo) conformation. InDeepNet provides an intuitive interface for researchers to upload protein structures from in-house data, the Protein Data Bank (PDB), or AlphaFold, predicting potential binding sites for proteins or small molecules. Results are presented as interactive 3D visualizations via Mol*, facilitating structural analysis. With InDeepHolo, the platform helps select conformations optimal for small-molecule binding, improving structure-based drug design. Accessible at https://indeep-net.gpu.pasteur.cloud/, InDeepNet removes the need for specialized coding skills or high-performance computing, making advanced predictive models widely available. By streamlining PPI target assessment and ligandability prediction, it assists research and supports therapeutic development targeting PPIs.

PMID:40337922 | DOI:10.1093/nar/gkaf403

Theor Appl Genet. 2025 May 8;138(6):111. doi: 10.1007/s00122-025-04892-1.

ABSTRACT

Finger millet (Eleusine coracana subsp. coracana) (2n = 4x = 36) remains one of the most important millets in East Africa (EA), where it was most likely domesticated along the highlands of Ethiopia and Uganda. The goal of the current study was to understand the population structure of the Ethiopian finger millet landraces and identify quantitative trait nucleotides (QTNs) and haplotypes associated with agronomic and nutritional traits. In a field evaluation across three environments, 448 genotypes were assessed for days to flowering (DTF), days to maturity (DTM), thousand seed weight (TSW), grain yield (GY), stay-green score (STG), and drought score (DrtSc). The harvested grain was analyzed for Fe and Zn contents. A subset of 391 genotypes was skim-sequenced, generating 24,112 high-quality SNPs that were employed for population structure, association mapping, and haplotype analysis. Seventy marker-trait associations were detected including 15 major QTNs with more than 30% phenotypic variance explained (PVE) for all traits except STG and GY. Pleiotropic major QTNs were identified for DTM/DTF and Fe/Zn on chromosomes 9B and 2B, respectively. Haplotype analysis of major QTNs identified 54 significant haplotype blocks and 2 additional haplotypes for a multidrug ABC transporter gene family like protein on chromosome 4A that was associated with PTH. Favorable haplotypes from pleiotropic DTM/DTF and Fe/Zn QTNs were present in 13 and 12 genotypes respectively, majority from Tigray region. Two genotypes from Tigray and one from Amhara harbored favorable haplotypes for DTM/DTF and Fe/Zn. These findings provide invaluable insights for targeted breeding to enhance finger millet resilience, nutritional profile, and yield.

PMID:40338316 | DOI:10.1007/s00122-025-04892-1

Genome. 2025 Jan 1;68:1-12. doi: 10.1139/gen-2024-0106.

ABSTRACT

Soybean seeds are rich in oil and protein; however, the seed composition is influenced by genotype and environment. For years, it has been observed that soybeans grown in western Canada have lower seed protein concentration (by ∼1%-5% total seed weight) than those grown in eastern Canada. In this study, soybean seeds harvested from five varieties were grown in four different locations in Canada (east and west growing regions) and analyzed using RNA-sequencing. Using gene ontology and biological pathway mapping, we identified a difference in cysteine and methionine metabolism between soybeans grown in eastern and western Canada that may attribute to the difference in seed protein concentration. Further, we identified differential gene expression within the oil biosynthesis pathway, specifically upregulation of lipoxygenases in western-grown soybeans, which may also influence seed composition and/or membrane fluidity. The information gained in this study is useful for marker assisted selection in soybean breeding programs across Canada and globally.

PMID:40338102 | DOI:10.1139/gen-2024-0106

J Diabetes Sci Technol. 2025 May 8:19322968251338865. doi: 10.1177/19322968251338865. Online ahead of print.

ABSTRACT

BACKGROUND: Continuous glucose monitors (CGMs) are increasingly being used to guide glucose management in the hospital. However, uncertainty regarding their accuracy in this setting remains.

METHODS: We conducted a nonrandomized, open-label, clinically blinded prospective trial of the Dexcom G6 Pro (G6P) and FreeStyle Libre Pro (FLP) in the inpatient setting among critically ill hospitalized patients (n = 40) requiring continuous intravenous insulin infusion. In parallel with CGM data, reference serum (Lab) glucose and point-of-care (POC) glucose values were obtained. On completion of the study, CGM and reference values were analyzed to assess CGM accuracy.

RESULTS: A total of 1015 matched G6P-Lab pairs had a mean absolute relative difference (MARD) of 22.7%, 2369 G6P-POC pairs had an MARD of 22.9%, 1006 matched FLP-Lab pairs had an MARD of 25.2%, and 2353 FLP-POC pairs had an MARD of 27.0%. Both CGM systems demonstrated considerable inter-patient variability in sensor accuracy and tended to underestimate glucose in comparison with the reference values. Rarely were low reference values overestimated by either sensor.

CONCLUSIONS: Factory-calibrated continuous glucose monitors may require accuracy validation and per-patient calibration for inpatient use in critically ill patients.

PMID:40337990 | DOI:10.1177/19322968251338865

Nucleic Acids Res. 2025 May 8:gkaf384. doi: 10.1093/nar/gkaf384. Online ahead of print.

ABSTRACT

Changes in alternative splicing between groups or conditions contribute to protein-protein interaction rewiring, a consequence often neglected in data analysis. The web server and database DIGGER overcomes this limitation by augmenting a protein-protein interaction network with domain-domain interactions and splicing information. Here, we present DIGGER 2.0, which now features both experimental and newly added predicted domain-domain interactions. In addition to the human interactome, DIGGER 2.0 adds support for mouse as an important model organism. Additionally, we integrated the splicing analysis tool NEASE, which allows users to perform online splicing- and interactome-informed enrichment analysis on RNA-seq data. In two application cases (multiple sclerosis and mice models of cardiac diseases), we show the utility of DIGGER 2.0 for deeper exploration and functional interpretation of changes in alternative splicing in human and mouse disorders. DIGGER 2.0 is available at https://exbio.wzw.tum.de/digger/.

PMID:40337913 | DOI:10.1093/nar/gkaf384

Ann Bot. 2025 May 4:mcaf079. doi: 10.1093/aob/mcaf079. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Dry dehiscent fruits have independently evolved multiple times during angiosperm diversification. A striking example is the convergent evolution of Brassicaceae siliques and Papaveraceae pods, both formed by two fused carpels forming valves, that meet at a replum or replum-like structure. In both cases, valve separation occurs through a dehiscence zone at the valve margins in contact with the replum. In Arabidopsis, fruit development is regulated by transcription factors: FRUITFULL (FUL) ensures proper valve cell division, REPLUMLESS (RPL) specifies replum identity, and SHATTERPROOF (SHP1/2) genes pattern the dehiscence zone. SHP1/2 also regulate INDEHISCENT (IND) for lignified layer formation and ALCATRAZ (ALC) and SPATULA (SPT) for the non-lignified layer, with the network antagonized by APETALA2 (AP2), which influences replum formation and valve margin growth.

METHODS: Using previously published and new In situ RNA hybridization expression data, we evaluated how this network applies to basal eudicots.

KEY RESULTS: In Bocconia frutescens, homolog expression suggests conserved roles for FUL and AP2 in fruit wall proliferation, acting antagonistically to ALC and RPL homologs localized to the dehiscence zone. A role for STK homologs in dehiscence zone formation cannot be excluded, while the role of AG-like genes, the closest homologs of SHP during fruit development is unlikely.

CONCLUSIONS: Our findings indicate significant rewiring of the fruit developmental network between basal and core eudicots, underscoring the need for functional studies in non-eudicot species to validate this framework.

PMID:40337869 | DOI:10.1093/aob/mcaf079

Philos Trans A Math Phys Eng Sci. 2025 May 8;383(2296):20230384. doi: 10.1098/rsta.2023.0384. Epub 2025 May 8.

ABSTRACT

Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of 'reductionism and integrationism' seems less controversial today, a new apparent dichotomy dominates discussions: mechanistic versus data-driven modelling. In light of this distinction, we provide an overview of recent achievements and new challenges with a focus on the cardiovascular system. Attention has shifted from generating a universal model of the human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable in silico clinical trials. Disease-specific parametrization, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardized and quality-controlled data are important issues today, which call for open tools, data and metadata standards, as well as strong community interactions. The quantitative, biophysical and highly controlled approach provided by in silico methods has become an integral part of physiological and medical research. In silico methods have the potential to accelerate future progress also in the fields of integrated multi-physics modelling, multi-scale models, virtual cohort studies and machine learning beyond what is feasible today. In fact, mechanistic and data-driven modelling can complement each other synergistically and fuel tomorrow's artificial intelligence applications to further our understanding of physiology and disease mechanisms, to generate new hypotheses and assess their plausibility, and thus to contribute to the evolution of preventive, diagnostic and therapeutic approaches.This article is part of the theme issue 'Science into the next millennium: 25 years on'.

PMID:40336283 | DOI:10.1098/rsta.2023.0384