Front Oncol. 2024 Nov 26;14:1519816. doi: 10.3389/fonc.2024.1519816. eCollection 2024.

NO ABSTRACT

PMID:39659796 | PMC:PMC11628518 | DOI:10.3389/fonc.2024.1519816

Bioinform Adv. 2024 Nov 14;4(1):vbae178. doi: 10.1093/bioadv/vbae178. eCollection 2024.

ABSTRACT

MOTIVATION: The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization.

RESULTS: To make explorative analysis of longitudinal data readily available to researchers without formal background in computer science and programming, we present MEtabolite Trajectory ExplORer (MeTEor). MeTEor is an R Shiny app providing a comprehensive set of statistical analysis methods. To demonstrate the capabilities of MeTEor, we replicated the analysis of metabolomics data from a previously published study on COVID-19 patients.

AVAILABILITY AND IMPLEMENTATION: MeTEor is available as an R package and as a Docker image. Source code and instructions for setting up the app can be found on GitHub (https://github.com/scibiome/meteor). The Docker image is available at Docker Hub (https://hub.docker.com/r/gordomics/meteor). MeTEor has been tested on Microsoft Windows, Unix/Linux, and macOS.

PMID:39659589 | PMC:PMC11631383 | DOI:10.1093/bioadv/vbae178

Front Plant Sci. 2024 Nov 26;15:1484616. doi: 10.3389/fpls.2024.1484616. eCollection 2024.

ABSTRACT

Bacteria associated with plants play crucial roles in promoting plant growth and health by aiding in nutrient acquisition, including phosphorus. This study presents the isolation and genomic characterization of a potentially new bacterial strain, Lelliottia sp. JS-SCA-14, which exhibits significant plant growth-promoting effects through phosphorus solubilization. A comparative phylogenomic analysis of the complete genome of strain JS-SCA-14 and its closely related strains revealed a unique genomic profile, suggesting it could be a novel species. Genomic identity calculations indicated that JS-SCA-14 significantly deviates from strains belonging to closely related genera, such as Buttiauxella, Citrobacter, Enterobacter, Leclercia, and Lelliottia. A biochemical assay comparing JS-SCA-14 and a closely related strain, Lelliottia jeotgali PFL01T, showed differing patterns in carbon source utilization and enzyme activities. To assess the plant growth-promoting capabilities of strain JS-SCA-14, tests were conducted to evaluate its siderophore-producing and phosphate-solubilizing abilities. Seed germination assays demonstrated an improvement in germination, seedling length, and vigor compared to untreated controls. Notably, the phosphate-dissolving strain JS-SCA-14 led to a significant increase of 34.4% in fresh weight and 35.4% in dry weight of tomato plants compared to the negative control. These findings underscore the significant potential of strain JS-SCA-14 in solubilizing phosphate, thereby enhancing phosphorus availability in the rhizosphere and promoting plant growth and development. This study contributes to our understanding of plant-microbe interactions and suggests the potential application of strain JS-SCA-14 as a bioinoculant for sustainable agriculture and plant nutrient management strategies.

PMID:39659413 | PMC:PMC11628249 | DOI:10.3389/fpls.2024.1484616

Adv Sci (Weinh). 2024 Dec 10:e2409160. doi: 10.1002/advs.202409160. Online ahead of print.

ABSTRACT

Epigenetic control of cell fates is a critical determinant to maintain cell type stability and permit differentiation during embryonic development. However, the epigenetic control mechanisms are not well understood. Here, it is shown that the histone acetyltransferase reader protein BRD8 impairs the conversion of primed mouse EpiSCs (epiblast stem cells) to naive mouse ESCs (embryonic stem cells). BRD8 works by maintaining histone acetylation on promoters and transcribed gene bodies. BRD8 is responsible for maintaining open chromatin at somatic genes, and histone acetylation at naive-specific genes. When Brd8 expression is reduced, chromatin accessibility is unchanged at primed-specific genes, but histone acetylation is reduced. Conversely, naive-specific genes has reduced repressive chromatin marks and acquired accessible chromatin more rapidly during the cell type conversion. It is shown that this process requires active histone deacetylation to promote the conversion of primed to naive. This data supports a model for BRD8 reading histone acetylation to accurately localize the genome-wide binding of the histone acetyltransferase KAT5. Overall, this study shows how the reading of the histone acetylation state by BRD8 maintains cell type stability and both enables and impairs stem cell differentiation.

PMID:39656858 | DOI:10.1002/advs.202409160

J Exp Bot. 2024 Dec 4;75(22):6893-6897. doi: 10.1093/jxb/erae439.

NO ABSTRACT

PMID:39656674 | DOI:10.1093/jxb/erae439

Brief Bioinform. 2024 Nov 22;26(1):bbae633. doi: 10.1093/bib/bbae633.

ABSTRACT

Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating. Six datasets from diverse species and sample types were included in the study, and manual gating with two hierarchical layers was used as the truth for evaluation. For manual gating, results from five researchers were compared to illustrate the gating consistency among different raters. For unsupervised clustering, 23 tools were quantitatively compared in terms of accuracy with the truth and computing cost. While no method outperformed all others, several tools, including PAC-MAN, CCAST, FlowSOM, flowClust, and DEPECHE, generally demonstrated strong performance. For supervised auto-gating methods, four algorithms were evaluated, where DeepCyTOF and CyTOF Linear Classifier performed the best. We further provided practical recommendations on prioritizing gating methods based on different application scenarios. This study offers comprehensive insights for biologists to understand diverse gating methods and choose the best-suited ones for their applications.

PMID:39656848 | DOI:10.1093/bib/bbae633

Brief Bioinform. 2024 Nov 22;26(1):bbae628. doi: 10.1093/bib/bbae628.

ABSTRACT

Breast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion of sophisticated computational methodologies with extensive biological datasets has emerged as an effective strategy for unravelling complex patterns in cancer oncology. This research delves into breast cancer staging, classification, and diagnosis by leveraging the comprehensive dataset provided by the The Cancer Genome Atlas (TCGA). By integrating advanced machine learning algorithms with bioinformatics analysis, it introduces a cutting-edge methodology for identifying complex molecular signatures associated with different subtypes and stages of breast cancer. This study utilizes TCGA gene expression data to detect and categorize breast cancer through the application of machine learning and systems biology techniques. Researchers identified differentially expressed genes in breast cancer and analyzed them using signaling pathways, protein-protein interactions, and regulatory networks to uncover potential therapeutic targets. The study also highlights the roles of specific proteins (MYH2, MYL1, MYL2, MYH7) and microRNAs (such as hsa-let-7d-5p) that are the potential biomarkers in cancer progression founded on several analyses. In terms of diagnostic accuracy for cancer staging, the random forest method achieved 97.19%, while the XGBoost algorithm attained 95.23%. Bioinformatics and machine learning meet in this study to find potential biomarkers that influence the progression of breast cancer. The combination of sophisticated analytical methods and extensive genomic datasets presents a promising path for expanding our understanding and enhancing clinical outcomes in identifying and categorizing this intricate illness.

PMID:39656775 | DOI:10.1093/bib/bbae628

Nat Cardiovasc Res. 2024 Dec 10. doi: 10.1038/s44161-024-00574-1. Online ahead of print.

ABSTRACT

The basic helix-loop-helix transcription factor HAND2 has multiple roles during vertebrate organogenesis, including cardiogenesis. However, much remains to be uncovered about its mechanism of action. Here, we show the generation of several hand2 mutant alleles in zebrafish and demonstrate that dimerization-deficient mutants display the null phenotype but DNA-binding-deficient mutants do not. Rescue experiments with Hand2 variants using a newly identified hand2 enhancer confirmed these observations. To identify Hand2 effectors critical for cardiogenesis, we analyzed the transcriptomes of hand2 loss- and gain-of-function embryonic cardiomyocytes and tested the function of eight candidate genes in vivo; pdgfra was most effective in rescuing myocardial migration in hand2 mutants. Accordingly, we identified a putative Hand2-binding region in the zebrafish pdgfra locus that is important for its expression. In addition, Hand2 loss- and gain-of-function experiments in mouse embryonic stem cell-derived cardiac cells decreased and increased Pdgfra expression, respectively. Altogether, these results further our mechanistic understanding of HAND2 function during early cardiogenesis.

PMID:39658721 | DOI:10.1038/s44161-024-00574-1

Int J Obes (Lond). 2024 Dec 10. doi: 10.1038/s41366-024-01695-0. Online ahead of print.

ABSTRACT

BACKGROUND: Human urine is highly favorable for 1H NMR metabolomics analyses of obesity-related diseases, such as non-alcoholic fatty liver, type 2 diabetes, and hyperlipidemia (HL), due to its non-invasiveness and ease of large-scale collection. However, the wide range of intrinsic urine pH (5.5-8.5) results in inevitably chemical shift and signal intensity modulations in the 1H NMR spectra. For patients where acidic urine pH is closely linked to obesity-related disease phenotypes, the pH-dependent modulations complicate the spectral analysis and deteriorate quantifications of urine metabolites.

METHODS: We characterized human urine metabolites by NMR at intrinsic urine pH, across urine pH 4.5 to 9.5, to account for pH-dependent modulations. A pH-dependent chemical shift database for quantifiable urine metabolites was generated and integrated into a "pH intelligence" program developed for quantifications of pH-dependent modulations at various pH. The 1H NMR spectra of urines collected from patients with Ob-HL and healthy controls were compared to uncover potential metabolic biomarkers of Ob-HL disease.

RESULTS: Three urine metabolites were unveiled by pH-dependent NMR approach, i.e., TMAO, glycine, and pyruvic acid, with VIP score >1.0 and significant q-value < 0.05, that represent as potential biomarkers for discriminating Ob-HL from healthy controls. Further ROC-AUC analyses revealed that TMAO alone achieved the highest diagnostic accuracy (AUC 0.902), surpassed to that obtained by neutralizing pH approach (AUC 0.549) and enabled better recovering potential urine metabolites from the Ob-HL disease phenotypes.

CONCLUSIONS: We concluded that 1H NMR-derived urine metabolite profile represents a snapshot that can reveal the physiological condition of humans in either a healthy or diseased state under intrinsic urine pH. We demonstrated a systematic analysis of pH-dependent modulations on the human urine metabolite signals and further developed software for quantification of urine metabolite profiles with high accuracy, enabling the uncovering of potential metabolite biomarkers in clinical diagnosis applications.

PMID:39658677 | DOI:10.1038/s41366-024-01695-0

Neuropsychopharmacology. 2024 Dec 10. doi: 10.1038/s41386-024-02033-8. Online ahead of print.

ABSTRACT

Use of one addictive drug typically influences the behavioral response to other drugs, either administered at the same time or a subsequent time point. The nature of the drugs being used, as well as the timing and dosing, also influence how these drugs interact. Here, we tested the effects of adolescent THC exposure on the development of morphine-induced behavioral adaptations following repeated morphine exposure during adulthood. We found that adolescent THC administration paradoxically prevented the development of anxiety-related behaviors that emerge during a forced abstinence period following morphine administration but facilitated reinstatement of morphine CPP. Following forced abstinence, we then mapped the whole-brain response to a moderate dose of morphine and found that adolescent THC administration led to an overall increase in brain-wide neuronal activity and increased the functional connectivity between frontal cortical regions and the ventral tegmental area. Last, we show using rabies virus-based circuit mapping that adolescent THC exposure triggers a long-lasting elevation in connectivity from the frontal cortex regions onto ventral tegmental dopamine cells. Our study adds to the rich literature on the interaction between drugs, including THC and opioids, and provides potential neural substates by which adolescent THC exposure influences responses to morphine later in life.

PMID:39658631 | DOI:10.1038/s41386-024-02033-8

Gigascience. 2024 Jan 2;13:giae088. doi: 10.1093/gigascience/giae088.

ABSTRACT

BACKGROUND: Current experimental practices typically produce large multidimensional datasets. Distance matrix calculation between elements (e.g., samples) for such data, although being often necessary in preprocessing for statistical inference or visualization, can be computationally demanding. Data sparsity, which is often observed in various experimental data modalities, such as single-cell sequencing in bioinformatics or collaborative filtering in recommendation systems, may pose additional algorithmic challenges.

RESULTS: We present GPU-Assisted Distance Estimation Software (GADES), a graphical processing unit (GPU)-enhanced package that allows for massively paralleled Kendall-$\tau$ distance matrices computation. The package's architecture involves specific memory management, which lifts the limits for the data size imposed by GPU memory capacity. Additional algorithmic solutions provide a means to address the data sparsity problem and reinforce the acceleration effect for sparse datasets. Benchmarking against available central processing unit-based packages on simulated and real experimental single-cell RNA sequencing or single-cell ATAC sequencing datasets demonstrated significantly higher speed for GADES compared to other methods for both sparse and dense data processing, with additional performance boost for the sparse data.

CONCLUSIONS: This work significantly contributes to the development of computational strategies for high-performance Kendall distance matrices computation and allows for the efficient processing of Big Data with the power of GPU. GADES is freely available at https://github.com/lab-medvedeva/GADES-main.

PMID:39658191 | DOI:10.1093/gigascience/giae088

Food Res Int. 2025 Jan;199:115369. doi: 10.1016/j.foodres.2024.115369. Epub 2024 Nov 20.

ABSTRACT

Probiotic foods are foods containing probiotics, including dairy and non-dairy products, that exert significant beneficial impacts on human health. Benefiting from the rapid progress in systems biology, diverse types of probiotics with prominent health-promoting functionalities are unraveled, albeit such functions could be substantially influenced by the stress environments. Here, we conducted a comprehensive review to characterize the state-of-the-art research on probiotic foods and specific probiotics employed in their production. We summarized the detrimental effects of various environmental stresses, including those encountered during industrial fermentation and storage (in vitro), as well as in vivo conditions such as digestion and intestinal colonization, on the biological functions of probiotics. Furthermore, this review outlines the recent advancements in elucidating the mechanisms of stress resistance, which are expected to enhance targeted probiotic applications and optimize their functional properties. Additionally, we summarized various strategies aimed at improving stress tolerance by regulating cell physiological function, specifically adaptive laboratory evolution, preadaptation treatment, exogenous supplementation, and molecular biological manipulation. This review underscores the significance of enhancing our understanding of stress tolerance mechanisms at a systems level and developing efficacious anti-stress strategies to enhance the application of probiotics while maximizing their biological functionalities.

PMID:39658167 | DOI:10.1016/j.foodres.2024.115369

Nucleic Acids Res. 2024 Dec 10:gkae1220. doi: 10.1093/nar/gkae1220. Online ahead of print.

ABSTRACT

The 2025 Nucleic Acids Research database issue contains 185 papers spanning biology and related areas. Seventy three new databases are covered, while resources previously described in the issue account for 101 update articles. Databases most recently published elsewhere account for a further 11 papers. Nucleic acid databases include EXPRESSO for multi-omics of 3D genome structure (this issue's chosen Breakthrough Resource and Article) and NAIRDB for Fourier transform infrared data. New protein databases include structure predictions for human isoforms at ASpdb and for viral proteins at BFVD. UniProt, Pfam and InterPro have all provided updates: metabolism and signalling are covered by new descriptions of STRING, KEGG and CAZy, while updated microbe-oriented databases include Enterobase, VFDB and PHI-base. Biomedical research is supported, among others, by ClinVar, PubChem and DrugMAP. Genomics-related resources include Ensembl, UCSC Genome Browser and dbSNP. New plant databases cover the Solanaceae (SolR) and Asteraceae (AMIR) families while an update from NCBI Taxonomy also features. The Database Issue is freely available on the Nucleic Acids Research website (https://academic.oup.com/nar). At the NAR online Molecular Biology Database Collection (http://www.oxfordjournals.org/nar/database/c/), 932 entries have been reviewed in the last year, 74 new resources added and 226 discontinued URLs eliminated bringing the current total to 2236 databases.

PMID:39658041 | DOI:10.1093/nar/gkae1220

Plant Cell Physiol. 2024 Dec 10:pcae144. doi: 10.1093/pcp/pcae144. Online ahead of print.

NO ABSTRACT

PMID:39657984 | DOI:10.1093/pcp/pcae144

Am J Physiol Lung Cell Mol Physiol. 2024 Dec 10. doi: 10.1152/ajplung.00048.2024. Online ahead of print.

ABSTRACT

Pulmonary arterial hypertension (PAH) is a progressive disease driven by endothelial cell inflammation and dysfunction, resulting in the pathological remodeling of the pulmonary vasculature. Innate immune activation has been linked to PAH development; however, the regulation, propagation, and reversibility of the induction of inflammation in PAH is poorly understood. Here, we demonstrate a role for interferon inducible protein 16 (IFI16), an innate immune sensor, as a modulator of endothelial inflammation in pulmonary hypertension, utilizing human pulmonary artery endothelial cells (PAECs). Inflammatory stimulus of PAECs with IL-1b up-regulates IFI16 expression, inducing proinflammatory cytokine up-regulation and cellular apoptosis. IFI16 mRNA stability is regulated by post-transcriptional m6A modification, mediated by Wilms' tumor 1-associated protein (WTAP), a structural stabilizer of the methyltransferase complex, via regulation of m6A methylation of IFI16. Additionally, m6A levels are increased in the peripheral blood mononuclear cells of PAH patients compared to control, indicating that quantifying this epigenetic change in patients may hold potential as a biomarker for disease identification. In summary, our study demonstrates IFI16 mediates inflammatory endothelial pathophenotypes seen in pulmonary arterial hypertension.

PMID:39657959 | DOI:10.1152/ajplung.00048.2024

Nucleic Acids Res. 2024 Dec 9:gkae1115. doi: 10.1093/nar/gkae1115. Online ahead of print.

ABSTRACT

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.

PMID:39657789 | DOI:10.1093/nar/gkae1115

Cell Rep Methods. 2024 Dec 6:100914. doi: 10.1016/j.crmeth.2024.100914. Online ahead of print.

ABSTRACT

Mode of inheritance (MOI) is necessary for clinical interpretation of pathogenic variants; however, the majority of variants lack this information. Furthermore, variant effect predictors are fundamentally insensitive to recessive-acting diseases. Here, we present MOI-Pred, a variant pathogenicity prediction tool that accounts for MOI, and ConMOI, a consensus method that integrates variant MOI predictions from three independent tools. MOI-Pred integrates evolutionary and functional annotations to produce variant-level predictions that are sensitive to both dominant-acting and recessive-acting pathogenic variants. Both MOI-Pred and ConMOI show state-of-the-art performance on standard benchmarks. Importantly, dominant and recessive predictions from both tools are enriched in individuals with pathogenic variants for dominant- and recessive-acting diseases, respectively, in a real-world electronic health record (EHR)-based validation approach of 29,981 individuals. ConMOI outperforms its component methods in benchmarking and validation, demonstrating the value of consensus among multiple prediction methods. Predictions for all possible missense variants are provided in the "Data and code availability" section.

PMID:39657681 | DOI:10.1016/j.crmeth.2024.100914

Horm Behav. 2024 Dec 9;167:105672. doi: 10.1016/j.yhbeh.2024.105672. Online ahead of print.

ABSTRACT

Gonadal steroid hormones are thought to activate sexual behavior by actions on multiple organ systems, including the nervous system and genitalia. We previously characterized ovarian hormone dependent behavioral and neural responses to clitoral stimulation in female mice. Here we investigate whether sex differences exist in the responses to tactile genital stimulation, and whether these might depend on gonadal androgens. We measured conditioned place preference (CPP) in response to manual tactile stimulation of either the prepuce or dorsum and subsequently measured neural activation. Behavioral and neural responses to genital stimulation were sexually equivalent in gonadally intact mice, with males exhibiting CPP and neural activation responses similar to those previously reported in females, with the exception of the Arcuate nucleus, which was activated to a greater extent in females. An unexpected sex difference in response to dorsal stimulation was observed, with only males developing CPP and increased FOS expression in the nucleus accumbens. Unlike females, the reward value of tactile stimulation was unaffected by gonadectomy in males. However, neural responses to tactile stimulation were disrupted by gonadectomy in both sexes. Testosterone treatment was only partially effective in restoring neural responses to genital stimulation and did so in a sexually diffentiated manner. We conclude that behavioral and neural responses of sexually-naïve mice to genital stimulation are largely similar between males and females, but that non-genital tactile stimulation is more reinforcing to males. Further, the relationship between gonadal steroid hormones and genital reward is sexually differentiated.

PMID:39657388 | DOI:10.1016/j.yhbeh.2024.105672

Elife. 2024 Dec 10;13:RP94929. doi: 10.7554/eLife.94929.

ABSTRACT

Bacterial membranes are complex and dynamic, arising from an array of evolutionary pressures. One enzyme that alters membrane compositions through covalent lipid modification is MprF. We recently identified that Streptococcus agalactiae MprF synthesizes lysyl-phosphatidylglycerol (Lys-PG) from anionic PG, and a novel cationic lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), from neutral glycolipid Glc-DAG. This unexpected result prompted us to investigate whether Lys-Glc-DAG occurs in other MprF-containing bacteria, and whether other novel MprF products exist. Here, we studied protein sequence features determining MprF substrate specificity. First, pairwise analyses identified several streptococcal MprFs synthesizing Lys-Glc-DAG. Second, a restricted Boltzmann machine-guided approach led us to discover an entirely new substrate for MprF in Enterococcus, diglucosyl-diacylglycerol (Glc2-DAG), and an expanded set of organisms that modify glycolipid substrates using MprF. Overall, we combined the wealth of available sequence data with machine learning to model evolutionary constraints on MprF sequences across the bacterial domain, thereby identifying a novel cationic lipid.

PMID:39656516 | DOI:10.7554/eLife.94929

Microbiol Spectr. 2024 Dec 10:e0099624. doi: 10.1128/spectrum.00996-24. Online ahead of print.

ABSTRACT

The Amazon, an important biodiversity hotspot, remains poorly explored in terms of its microbial diversity and biotechnological potential. The present study characterized the metabolic potential of Gram-positive strains of the Actinomycetes and Bacilli classes isolated from soil samples of an Amazon Conservation Unit. The sequencing of the 16S rRNA gene classified the strains ACT015, ACT016, and FIR094 within the genera Streptomyces, Rhodococcus, and Brevibacillus, respectively. Genome mining identified 33, 17, and 14 biosynthetic gene clusters (BGCs) in these strains, including pathways for the biosynthesis of antibiotic and antitumor agents. Additionally, 40 BGCs (62,5% of the total BGCs) were related to unknown metabolites. The OSMAC approach and untargeted metabolomics analysis revealed a plethora of metabolites under laboratory conditions, underscoring the untapped chemical diversity and biotechnological potential of these isolates. Our findings illustrated the efficacy of the metabologenomics approach in elucidating secondary metabolism and selecting BGCs with chemical novelty.IMPORTANCEThe largest rainforest in the world is globally recognized for its biodiversity. However, until now, few studies have been conducted to prospect natural products from the Amazon microbiome. In this work, we isolated three free-living bacterial species from the microbiome of pristine soils and used two high-throughput technologies to reveal the vast unexplored repertoire of secondary metabolites produced by these microorganisms.

PMID:39656018 | DOI:10.1128/spectrum.00996-24