Fundam Res. 2023 Feb 19;5(1):215-227. doi: 10.1016/j.fmre.2023.01.009. eCollection 2025 Jan.

ABSTRACT

There are critical transition phenomena during the progression of many diseases. Such critical transitions are usually accompanied by catastrophic disease deterioration, and their prediction is of significant importance for disease prevention and treatment. However, predicting disease deterioration solely based on a single sample is a difficult problem. In this study, we presented the network information gain (NIG) method, for predicting the critical transitions or disease state based on network flow entropy from omics data of each individual. NIG can not only efficiently predict disease deteriorations but also detect their dynamic network biomarkers on an individual basis and further identify potential therapeutic targets. The numerical simulation demonstrates the effectiveness of NIG. Moreover, our method was validated by successfully predicting disease deteriorations and identifying their potential therapeutic targets from four real omics datasets, i.e., an influenza dataset and three cancer datasets.

PMID:40166114 | PMC:PMC11955047 | DOI:10.1016/j.fmre.2023.01.009

Plant Cell Environ. 2025 Mar 31. doi: 10.1111/pce.15494. Online ahead of print.

ABSTRACT

As the most prevalent RNA modification in eukaryotes, N6-methyladenosine (m6A) plays a crucial role in regulating various biological processes in plants, including embryonic development and flowering. However, the function of m6A RNA methyltransferase in moso bamboo remains poorly understood. In this study, we identified two m6A methyltransferases in moso bamboo, PheMTA1 and PheMTA2. Overexpression of PheMTA1 and PheMTA2 significantly promoted root development and enhanced salt tolerance in rice. Using the HyperTRIBE method, we fused PheMTA1 and PheMTA2 with ADARcdE488Q and introduced them into rice. RNA sequencing (RNA-seq) of the overexpressing rice identified the target RNAs bound by PheMTA1 and PheMTA2. PheMTA1 and PheMTA2 bind to OsATM3 and OsSF3B1, which were involved in the development of root and salt resistance. Finally, we revealed the effects of transcription or alternative splicing on resistance-related genes like OsRS33, OsPRR73, OsAPX2 and OsHAP2E, which are associated with the observed phenotype. In conclusion, our study demonstrates that the m6A methyltransferases PheMTA1 and PheMTA2 from moso bamboo are involved in root development and enhance plant resistance to salt stress.

PMID:40165397 | DOI:10.1111/pce.15494

Plant Genome. 2025 Jun;18(2):e70023. doi: 10.1002/tpg2.70023.

ABSTRACT

Ascochyta blight (AB) is one of the most devastating fungal diseases of chickpea (Cicer arietinum L.). Conventional breeding has focused on exploiting and introgressing major genes (qualitative effect) to improve AB resistance in released varieties. However, such approaches are time-consuming and prone to the breakdown of disease resistance due to the fast evolution of AB pathogen. Genomic selection (GS) offers a promising alternative by predicting breeding values using genome-wide single nucleotide polymorphisms (SNPs), regardless of major or minor effects. To our knowledge, this is the first study to develop and implement GS to improve AB resistance in chickpea. Over 4 years, 2790 chickpea lines, representing a broad range of germplasm collections primarily sourced from the Australian Grains Genebank, were evaluated for AB disease response in the field and in an outdoor pot-based facility. Plants were genotyped with the Illumina multispecies pulse 30K SNP array, resulting in 23,239 high-quality SNPs distributed across the genome. Intermediate-to-high genomic prediction accuracies (0.40-0.90) were achieved across validation scenarios. Bayesian modeling identified six major QTL explaining 33% of the genetic variance for AB resistance, with the remaining variance explained by minor effect genes. Using genomic estimated breeding values (GEBVs), 462 lines of the 2790 lines were predicted to have higher resistance compared to the released check varieties, revealing the potential of further improvement of AB resistance for the industry. The desirable genomic prediction accuracy obtained in the study supports the applicability of GS to breed for AB resistance in chickpea.

PMID:40164996 | DOI:10.1002/tpg2.70023

bioRxiv [Preprint]. 2025 Mar 15:2025.03.14.643192. doi: 10.1101/2025.03.14.643192.

ABSTRACT

Semilunar Granule Cells (SGCs) are sparse dentate gyrus projection neurons whose role in the dentate circuit, including pathway specific inputs, remains unknown. We report that SGCs receive more frequent spontaneous excitatory synaptic inputs than granule cells (GCs). Dual GC-SGC recordings identified that SGCs receive stronger medial entorhinal cortex and associational synaptic drive but lack short-term facilitation of lateral entorhinal cortex inputs observed in GCs. SGCs dendritic spine density in proximal and middle dendrites was greater than in GCs. However, the strength of commissural inputs and dendritic input integration, examined in passive morphometric simulations, were not different between cell types. Activity dependent labeling identified an overrepresentation of SGCs among neuronal ensembles in both mice trained in a spatial memory task and task naïve controls. The divergence of modality specific inputs to SGCs and GCs can enable parallel processing of information streams and expand the computational capacity of the dentate gyrus.

PMID:40161709 | PMC:PMC11952520 | DOI:10.1101/2025.03.14.643192

bioRxiv [Preprint]. 2025 Mar 16:2025.03.14.643160. doi: 10.1101/2025.03.14.643160.

ABSTRACT

The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current cell segmentation methods are prone to over- and under-segmentation, misassign transcripts to cells, require manual intervention, and suffer from low sensitivity and scalability. We introduce segger, a versatile graph neural network based on a heterogeneous graph representation of individual transcripts and cells, that frames cell segmentation as a transcript-to-cell link prediction task and can leverage single-cell RNA-seq information to improve transcript assignments. On multiple Xenium dataset benchmarks, segger exhibits superior sensitivity and specificity, while requiring orders of magnitude less compute time than existing methods. The user-friendly open-source software implementation has extensive documentation (https://elihei2.github.io/segger_dev/), requires little manual intervention, integrates seamlessly into existing workflows, and enables atlas-scale applications.

PMID:40161614 | PMC:PMC11952575 | DOI:10.1101/2025.03.14.643160

Plant Genome. 2025 Jun;18(2):e70019. doi: 10.1002/tpg2.70019.

ABSTRACT

Groundnut (Arachis hypogaea L.), also known as peanut, is an allotetraploid legume crop composed of two different progenitor sub-genomes. This crop is an important source for food, feed, and confectioneries. Leveraging translational genomics research has expedited the precision and speed in making selections of progenies in several crops through either marker-assisted selection or genomic selection, including groundnut. The availability of foundational genomic resources such as reference genomes for diploid progenitors and cultivated tetraploids, offered substantial opportunities for genomic interventions, including the development of genotyping assays. Here, a cost-effective and high-throughput genotyping assay has been developed with 5,081 single nucleotide polymorphisms (SNPs) referred to as "mid-density assay." This multi-purpose assay includes 5,000 highly informative SNPs selected based on higher polymorphism information content (PIC) from our previously developed high-density "Axiom_Arachis" array containing 58,233 SNPs. Additionally 82 SNPs associated with five resilience and quality traits were included for marker-assisted selection. To test the utility of the mid-density genotyping (MDG) assay, 2,573 genotypes from distinct sets of breeding populations were genotyped with the 5,081 SNPs. PIC of the SNPs in the MDG ranged from 0.34 to 0.37 among diverse sets. The first three principal components collectively explained 82.08% of the variance among these genotypes. The mid-density assay demonstrated a proficient ability to distinguish between the genotypes, offering a high level of genome-wide nucleotide diversity. This assay holds promise for possible deployment in the identification of varietal seed mixtures, genetic purity within gene bank germplasms and seed systems, foreground and background selection in backcross breeding programs, genomic selection, and sparse trait mapping studies in groundnut.

PMID:40164965 | DOI:10.1002/tpg2.70019

Nat Methods. 2025 Mar 31. doi: 10.1038/s41592-025-02643-0. Online ahead of print.

ABSTRACT

Data from the first phase of the Human Tumor Atlas Network (HTAN) are now available, comprising 8,425 biospecimens from 2,042 research participants profiled with more than 20 molecular assays. The data were generated to study the evolution from precancerous to advanced disease. The HTAN Data Coordinating Center (DCC) has enabled their dissemination and effective reuse. We describe the diverse datasets, how to access them, data standards, underlying infrastructure and governance approaches, and our methods to sustain community engagement. HTAN data can be accessed through the HTAN Portal, explored in visualization tools-including CellxGene, Minerva and cBioPortal-and analyzed in the cloud through the NCI Cancer Research Data Commons. Infrastructure was developed to enable data ingestion and dissemination through the Synapse platform. The HTAN DCC's flexible and modular approach to sharing complex cancer research data offers valuable insights to other data-coordination efforts and researchers looking to leverage HTAN data.

PMID:40164800 | DOI:10.1038/s41592-025-02643-0

Commun Biol. 2025 Mar 31;8(1):527. doi: 10.1038/s42003-025-07954-8.

ABSTRACT

Epigenomic mechanisms are critically involved in mediation of genetic and environmental factors that underlie cancer development. Histone modifications represent highly informative epigenomic marks that reveal activation and repression of gene activities and dysregulation of transcriptional control due to tumorigenesis. Here, we present a comprehensive epigenomic and transcriptomic mapping of 18 stage I and II tumor and 20 non-neoplastic tissues from non-small cell lung adenocarcinoma patients. Our profiling covers 5 histone marks including activating (H3K4me3, H3K4me1, and H3K27ac) and repressive (H3K27me3 and H3K9me3) marks and the transcriptome using only 20 mg of tissue per sample, enabled by low-input omic technologies. Using advanced integrative bioinformatic analysis, we uncover cancer-driving signaling cascade networks, changes in 3D genome modularity, differential expression and functionalities of transcription factors and noncoding RNAs. Many of these identified genes and regulatory molecules show no significant change in their expression or a single epigenomic modality, emphasizing the power of integrative multimodal and multiomic analysis using patient samples.

PMID:40164799 | DOI:10.1038/s42003-025-07954-8

Nat Genet. 2025 Mar 31. doi: 10.1038/s41588-025-02134-0. Online ahead of print.

ABSTRACT

Tobacco smoke, alone or combined with alcohol, is the predominant cause of head and neck cancer (HNC). We explore how tobacco exposure contributes to cancer development by mutational signature analysis of 265 whole-genome sequenced HNC samples from eight countries. Six tobacco-associated mutational signatures were detected, including some not previously reported. Differences in HNC incidence between countries corresponded with differences in mutation burdens of tobacco-associated signatures, consistent with the dominant role of tobacco in HNC causation. Differences were found in the burden of tobacco-associated signatures between anatomical subsites, suggesting that tissue-specific factors modulate mutagenesis. We identified an association between tobacco smoking and alcohol-related signatures, indicating a combined effect of these exposures. Tobacco smoking was associated with differences in the mutational spectra, repertoire of driver mutations in cancer genes and patterns of copy number change. Our results demonstrate the multiple pathways by which tobacco smoke can influence the evolution of cancer cell clones.

PMID:40164736 | DOI:10.1038/s41588-025-02134-0

Mol Psychiatry. 2025 Mar 31. doi: 10.1038/s41380-025-02973-7. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with a decade-long preclinical pathological period that can be divided into several stages. Emerging evidence has revealed that the microbiota-gut-brain axis plays an important role in AD pathology. However, the role of gut microbiota in different AD stages has not been well characterized. In this study, we performed fecal shotgun metagenomic analysis on a Chinese cohort with 476 participants across five stages of AD pathology to characterize stage-specific alterations in gut microbiota and evaluate their diagnostic potential. We discovered extensive gut dysbiosis that is associated with neuroinflammation and neurotransmitter dysregulation, with over 10% of microbial species and gene families showing significant alterations during AD progression. Furthermore, we demonstrated that microbial gene families exhibited strong diagnostic capabilities, evidenced by an average AUC of 0.80 in cross-validation and 0.75 in independent external validation. In the optimal model, the most discriminant gene families are primarily involved in the metabolism of carbohydrates, amino acids, energy, glycan and vitamins. We found that stage-specific microbial gene families in AD pathology could be validated by an in vitro gut simulator and were associated with specific genera. We also observed that the gut microbiota could affect the progression of cognitive decline in 5xFAD mice through fecal microbiota transplantation, which could be used for early intervention of AD. Our multi-stage large cohort metagenomic analysis demonstrates that alterations in gut microbiota occur from the very early stages of AD pathology, offering important etiological and diagnostic insights.

PMID:40164697 | DOI:10.1038/s41380-025-02973-7

PLoS Comput Biol. 2025 Mar 31;21(3):e1012927. doi: 10.1371/journal.pcbi.1012927. Online ahead of print.

ABSTRACT

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

PMID:40163550 | DOI:10.1371/journal.pcbi.1012927

J Vis Exp. 2025 Mar 14;(217). doi: 10.3791/67925.

ABSTRACT

Pure bacterial cultures are essential for the study of microbial culturomics. Traditional methods based on solid plates, well plates, and micro-reactors are hindered by cumbersome procedures and low throughput, impeding the rapid progress of microbial culturomics research. To address these challenges, we had successfully developed the Single-cell Microliter-droplet Culture Omics System (MISS cell), an automated high-throughput platform that utilizes droplet microfluidic technology for microbial monoclonal isolation, cultivation, and screening. This system can generate a large number of single-cell droplets and cultivate, screen, and collect monoclonal colonies in a short time, facilitating an integrated process from microbial isolation to picking. In this protocol, we demonstrated its application using the isolation and cultivation of human gut microbiota as an example and compared the microbial isolation efficiency, monoclonal culture performance, and screening throughput using the solid-plate culture method. The experimental workflow was simple, and reagent consumption was very low. Compared to solid-plate culture methods, the MISS cell could cultivate a greater diversity of gut microbiota species, offering significant potential and value for microbial culturomics research.

PMID:40163395 | DOI:10.3791/67925

Adv Sci (Weinh). 2025 Mar 31:e2414918. doi: 10.1002/advs.202414918. Online ahead of print.

ABSTRACT

The early detection of high-risk human papillomavirus (HR-HPV) is crucial for the assessment and improvement of prognosis in cervical cancer. However, existing PCR-based screening methods suffer from inadequate accessibility, which dampens the enthusiasm for screening among grassroots populations, especially in resource-limited areas, and contributes to the persistently high mortality rate of cervical cancer. Here, a portable system is proposed for multiplexed nucleic acid detection, termed R-CHIP, that integrates Recombinase polymerase amplification (RPA), CRISPR detection, Hand-driven microfluidics, and an artificial Intelligence Platform. The system can go from sample pre-processing to results readout in less than an hour with simple manual operation. Optimized for sensitivity of 10-17 M for HPV-16 and 10-18 M for HPV-18, R-CHIP has an accuracy of over 95% in 300 tests on clinical samples. In addition, a smartphone microimaging system combined with the ResNet-18 deep learning model is used to improve the readout efficiency and convenience of the detection system, with initial prediction accuracies of 96.0% and 98.0% for HPV-16 and HPV-18, respectively. R-CHIP, as a user-friendly and intelligent detection platform, has great potential for community-level HR-HPV screening in resource-constrained settings, and contributes to the prevention and early diagnosis of other diseases.

PMID:40163382 | DOI:10.1002/advs.202414918

Plant J. 2025 Apr;122(1):e70122. doi: 10.1111/tpj.70122.

ABSTRACT

We identified a mutant of hexaploid wheat (Triticum aestivum) with impaired responses to gravity. The mutant, named Twisted Sister1 (TS1), had agravitropic roots that were often twisted along with altered shoot phenotypes. Roots of TS1 were insensitive to externally applied auxin, with the genetics and physiology suggestive of a mutated AUX/IAA transcription factor gene. Hexaploid wheat possesses over 80 AUX/IAA genes, and sequence information did not identify an obvious candidate. Bulked segregant analysis of an F2 population mapped the mutation to chromosome 5A, and subsequent mapping located the mutation to a 41 Mbp region. RNA-seq identified the TraesCS5A03G0149800 gene encoding a TaAUX/IAA protein to be mutated in the highly conserved domain II motif. We confirmed TraesCS5A03G0149800 as underlying the mutant phenotype by generating transgenic Arabidopsis thaliana. Analysis of RNA-seq data suggested broad similarities between Arabidopsis and wheat for the role of AUX/IAA genes in gravity responses, although there were marked differences. Here we show that the sequenced wheat genome, along with previous knowledge of the physiology of gravity responses from other plant species, gene mapping, RNA-seq, and expression in Arabidopsis have enabled the cloning of a key wheat gene that defines plant architecture.

PMID:40162979 | DOI:10.1111/tpj.70122

Int J Immunopathol Pharmacol. 2025 Jan-Dec;39:3946320251324401. doi: 10.1177/03946320251324401. Epub 2025 Mar 31.

ABSTRACT

OBJECTIVE: This study aimed to investigate the relationship between fibroblast growth factor 19 (FGF19) and the prognosis and immune infiltration of colorectal cancer (CRC) and identify the related genes and pathways influencing the onset and progression of CRC.

INTRODUCTION: The potential of FGF19 to guide the prognosis of CRC and inform immunotherapeutic strategies warrants further investigation.

METHODS: We performed Quantitative Real-Time PCR to assess the expression of FGF19 and conducted a bioinformatics analysis to evaluate the impact of FGF19 expression on the clinical prognosis of CRC. We also analyzed the association between FGF19 expression and immune cell infiltration in CRC, and explored the related genes and pathways through which FGF19 influences CRC development.

RESULTS: CRC patients with higher FGF19 expression exhibited a poorer prognosis. In terms of the Receiver Operating Characteristic (ROC), FGF19 achieved an area under the curve (AUC) of 0.904. FGF19 expression correlated with the N stage, M stage, and pathological stage in patients with CRC. Functional enrichment analysis revealed significant enrichment of FGF19 in pathways associated with tumor development. ssGSEA and Spearman correlation analysis demonstrated that FGF19 expression was linked to tumor immune cells. We discovered that FGF19 is closely related to neutrophil extracellular traps (NETs), which play a significant role in the immune microenvironment.

CONCLUSION: FGF19 is a key gene associated with immunity and prognosis in CRC patients. Our findings suggest that FGF19 may influence CRC progression by promoting NETs expression, which leads to suppression of immune cells.

PMID:40162957 | DOI:10.1177/03946320251324401

Biomacromolecules. 2025 Mar 31. doi: 10.1021/acs.biomac.4c01798. Online ahead of print.

ABSTRACT

Biomedical device-related bacterial infections are a leading cause of mortality, and traditional antibiotics contribute to resistance. Various surface modification strategies have been explored, but effective clinical solutions remain limited. This study introduces a novel antibacterial nanocoating with copper nanoparticles (CuNPs) that triggers localized nitric oxide (NO) release. The multilayered nanocoating is created using branched polyethylenimine (BPEI) and poly(acrylic acid) (PAA) via a Layer-by-Layer assembly method. CuNP-decorated nanocoatings are formed by reducing copper ions coordinated with amine/carboxylic acid groups. In a physiological environment, CuNPs oxidize to Cu(I), promoting NO release from endogenous NO donors. The nanocoating's thickness is adjustable to regulate amount of CuNPs and NO flux. The optimal thickness for effective NO release against Staphylococcus aureus and Pseudomonas aeruginosa is identified, preventing microbial adhesion and biofilm formation. Importantly, the coating remains cytocompatible due to minimal CuNPs, physiological NO levels, and stable coating properties under physiological conditions.

PMID:40162566 | DOI:10.1021/acs.biomac.4c01798

Netw Neurosci. 2025 Mar 5;9(1):237-258. doi: 10.1162/netn_a_00433. eCollection 2025.

ABSTRACT

Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss (a) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics and (b) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

PMID:40161996 | PMC:PMC11949581 | DOI:10.1162/netn_a_00433

Netw Neurosci. 2025 Mar 5;9(1):159-180. doi: 10.1162/netn_a_00424. eCollection 2025.

ABSTRACT

Our brains operate as a complex network of interconnected neurons. To gain a deeper understanding of this network architecture, it is essential to extract simple rules from its intricate structure. This study aimed to compress and simplify the architecture, with a particular focus on interpreting patterns of functional connectivity in 2.5 hr of electrical activity from a vast number of neurons in acutely sliced mouse brains. Here, we combined two distinct methods together: automatic compression and network analysis. Firstly, for automatic compression, we trained an artificial neural network named NNE (neural network embedding). This allowed us to reduce the connectivity to features, be represented only by 13% of the original neuron count. Secondly, to decipher the topology, we concentrated on the variability among the compressed features and compared them with 15 distinct network metrics. Specifically, we introduced new metrics that had not previously existed, termed as indirect-adjacent degree and neighbor hub ratio. Our results conclusively demonstrated that these new metrics could better explain approximately 40%-45% of the features. This finding highlighted the critical role of NNE in facilitating the development of innovative metrics, because some of the features extracted by NNE were not captured by the currently existed network metrics.

PMID:40161994 | PMC:PMC11949542 | DOI:10.1162/netn_a_00424

Front Immunol. 2025 Mar 14;16:1536003. doi: 10.3389/fimmu.2025.1536003. eCollection 2025.

ABSTRACT

The T cell receptor (TCR) is crucial for immune responses and represents a pivotal therapeutic target for CAR T cell therapies. However, which enhancer elements drive the constitutive expression of the TCRα chain in mature, peripheral T cells has not been well defined. Earlier work has suggested that enhancer alpha is inactive in mature peripheral T cells and that an alternative enhancer element in the 5' J region was driving TRA expression, while more recent findings indicated the opposite. Here, we applied a pooled CRISPR screen to probe a large genomic region proximal to the human TRA gene for the presence of regulatory elements. Interestingly, no sgRNA targeting the 5' J region was identified that influenced TRA expression. In contrast, several sgRNAs targeting enhancer alpha element Tα2, were identified that compromised the expression of the TCRα chain in Jurkat E6.1, as well as in a subset of human primary T cells. Our results provide new insights into the regulation of TRA in human peripheral T cells, advancing our understanding of how constitutive TRA expression is driven and regulated.

PMID:40160815 | PMC:PMC11949936 | DOI:10.3389/fimmu.2025.1536003

iScience. 2025 Feb 28;28(3):112126. doi: 10.1016/j.isci.2025.112126. eCollection 2025 Mar 21.

ABSTRACT

Integrating enzyme parameters into constraint-based models have significantly improved the prediction of physiological and molecular traits. To further improve these models, we integrated promiscuous enzyme activities that jointly comprise the so-called underground metabolism by developing the CORAL toolbox, which increases the resolution of modeled enzyme resource allocation. Applying CORAL to a protein-constrained model of Escherichia coli revealed that underground metabolism resulted in larger flexibility of metabolic fluxes and enzyme usage. Simulating metabolic defects where the main activity of a promiscuous enzyme was blocked but promiscuous activities remained functional showed a small enzyme redistribution to the side activities. Further, blocking pairs of main activities showed that non-promiscuous enzymes exhibited larger impact on growth than promiscuous enzymes. These simulations showed that promiscuous enzymes can compensate for these defects, in line with experimental evidence. Together, our results indicated that promiscuous enzyme activities are vital to maintain robust metabolic function and growth.

PMID:40160425 | PMC:PMC11951047 | DOI:10.1016/j.isci.2025.112126