BMC Genomics. 2025 Apr 12;26(1):367. doi: 10.1186/s12864-025-11534-9.

ABSTRACT

BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined structure and lower sequence conservation-often yield low-confidence models. The latest Variant Effect Predictor (VEP), AlphaMissense, leverages AlphaFold2 models, achieving over 90% sensitivity and specificity in predicting variant effects. However, the effectiveness of tools for variants in disordered regions, which account for 30% of the human proteome, remains unclear.

RESULTS: In this study, we found that predicting pathogenicity for variants in disordered regions is less accurate than in ordered regions, particularly for mutations at the first N-Methionine site. Investigations into the efficacy of variant effect predictors on intrinsically disordered regions (IDRs) indicated that mutations in IDRs are predicted with lower sensitivity and the gap between sensitivity and specificity is largest in disordered regions, especially for AlphaMissense and VARITY.

CONCLUSIONS: The prevalence of IDRs within the human proteome, coupled with the increasing repertoire of biological functions they are known to perform, necessitated an investigation into the efficacy of state-of-the-art VEPs on such regions. This analysis revealed their consistently reduced sensitivity and differing prediction performance profile to ordered regions, indicating that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions.

PMID:40221640 | DOI:10.1186/s12864-025-11534-9

Trends Endocrinol Metab. 2025 Apr 11:S1043-2760(25)00054-2. doi: 10.1016/j.tem.2025.03.009. Online ahead of print.

ABSTRACT

Pyruvate kinase is a key regulator in hepatic glucose metabolism, encoded by the gene pyruvate kinase liver/red blood cells (PKLR). Systems biology-based approaches, including metabolic and gene co-expression networks analyses, as well as genome-wide association studies (GWAS), have led to the identification of PKLR as a pivotal gene influencing liver metabolism in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and hepatocellular carcinoma (HCC). Here, we review the critical role of PKLR in MASLD and HCC progression and examine the effects of PKLR modulation both in vitro and in vivo. We also discuss the development of therapeutic strategies for patients with MASLD and HCC by modulating PKLR, highlighting its promising future in a broader range of liver diseases.

PMID:40221236 | DOI:10.1016/j.tem.2025.03.009

Infect Genet Evol. 2025 Apr 10:105751. doi: 10.1016/j.meegid.2025.105751. Online ahead of print.

ABSTRACT

BACKGROUND: Diseases caused by pathogenic microorganisms pose a persistent global health challenge. Pathogens exploit host mechanisms through intricate molecular interactions. Understanding these host-pathogen interactions (HPIs), particularly protein-protein interactions (PPIs), is crucial for developing therapeutic strategies. While experimental approaches are essential, they are often labor-intensive and costly. Researchers have been able to predict HPIs more efficiently due to recent advances in artificial intelligence and machine learning. However, existing reviews lack a systematic evaluation of different machine learning methodologies and their effectiveness.

METHODS: This scoping review critically examines recent studies on machine learning-based Host-Pathogen Interaction (HPI) prediction, categorizing them by host and pathogen types, machine learning algorithms, and key evaluation metrics. The methodology is based on the study beginning with a preliminary search in reputable using key phrases related to host-pathogen interactions from 2019 to 2024. This process yielded 46 relevant articles, from which 30 were selected for review after evaluating titles and abstracts.

RESULTS: Our findings indicate that tree-based algorithms, particularly Random Forest and Gradient Boosting, are the most prevalent in Host-Pathogen Interaction (HPI) prediction. The filter articles were categorized by host and pathogen type and further subdivided into four subcategories based on the prediction type and machine learning algorithms: classic, tree-based, vector-based, and neural network algorithms. Convolutional and recurrent neural networks are among the deep learning models that demonstrate promising accuracy, but they require a lot of labeled data for effective training. Additionally, the analysis uncovers significant gaps in dataset standardization and model interpretability, which pose challenges to the broader applicability of these predictive models.

CONCLUSION: In this review, we emphasize the potential of machine learning in HPI prediction and highlight the important challenges that must be addressed to improve predictive accuracy. Unlike previous reviews, our study systematically compares different computational approaches, offering a roadmap for future research. The findings emphasize the importance of dataset quality, feature selection, and model transparency in advancing AI-driven pathogen research.

PMID:40220943 | DOI:10.1016/j.meegid.2025.105751

Cancer Lett. 2025 Apr 10:217715. doi: 10.1016/j.canlet.2025.217715. Online ahead of print.

ABSTRACT

Esophageal squamous cell carcinoma (ESCC) stands out as a common cancer type worldwide, characterized by its notably high rates of occurrence and mortality. The epidermal growth factor receptor (EGFR) is one of the main targets for cancer treatment as it is one of the genes whose expression is often altered by overexpression, amplification, and mutation in a variety of solid tumors. Substantial efforts have been made to develop EGFR-targeted therapeutic agents, including monoclonal antibodies and tyrosine kinase inhibitors (TKIs). However, these agents exhibited limited efficacy due to the emergence of acquired resistance. Therefore, novel treatment strategies targeting EGFR are urgently needed. Recent studies have identified a few natural compounds that can efficiently inhibit EGFR, indicating that natural products may be potential sources for the development of new EGFR inhibitors. Here, using the Drug Affinity Responsive Target Stability (DARTS) assay combined with liquid chromatography/tandem mass spectrometry analysis, co-crystal method, we discovered that Bufalin directly interacts with EGFR and causes EGFR endocytosis and degradation in the lysosome. Moreover, Bufalin exhibits superior anti-tumor activity compared with another EGFR TKIs. Our study identified Bufalin as the first natural small-molecule EGFR degrader, which suppresses EGFR signaling by inducing the degradation of EGFR via the endosome-lysosome pathway.

PMID:40220852 | DOI:10.1016/j.canlet.2025.217715

Cell Metab. 2025 Apr 8:S1550-4131(25)00116-0. doi: 10.1016/j.cmet.2025.03.013. Online ahead of print.

ABSTRACT

Polycystic ovary syndrome (PCOS), the most common endocrinopathy in women, causes significant reproductive and metabolic comorbidities, with no current cure. Gestational androgen and anti-Müllerian hormone (AMH) excess are linked to PCOS, and prenatal aberrant exposure to these hormones induces PCOS-like traits in animal models. However, whether the AMH effects on PCOS programming could extend to early postnatal life remains unknown. Clinical observations showed higher AMH levels during minipuberty in infants of mothers with PCOS, but whether this contributes to PCOS development is uncertain. Here, we show that exposure to high AMH levels during minipuberty in mice causes PCOS-like reproductive and metabolic defects in both sexes. A neutralizing antibody targeting AMH receptor 2 (AMHR2) prevented these defects when administered during minipuberty and alleviated symptoms when given in adulthood. These findings highlight the causal role of elevated AMH in PCOS and suggest AMHR2-targeting therapy as a potential preventive or curative approach.

PMID:40220763 | DOI:10.1016/j.cmet.2025.03.013

Cell Syst. 2025 Apr 9:101261. doi: 10.1016/j.cels.2025.101261. Online ahead of print.

ABSTRACT

Tumors are complex ecosystems composed of malignant and non-malignant cells embedded in a dynamic extracellular matrix (ECM). In the tumor microenvironment, molecular phenotypes are controlled by cell-cell and ECM interactions in 3D cellular neighborhoods (CNs). While their inhibition can impede tumor progression, routine molecular tumor profiling fails to capture cellular interactions. Single-cell spatial transcriptomics (ST) maps receptor-ligand interactions but usually remains limited to 2D tissue sections and lacks ECM readouts. Here, we integrate 3D ST with ECM imaging in serial sections from one clinical lung carcinoma to systematically quantify molecular states, cell-cell interactions, and ECM remodeling in CN. Our integrative analysis pinpointed known immune escape and tumor invasion mechanisms, revealing several druggable drivers of tumor progression in the patient under study. This proof-of-principle study highlights the potential of in-depth CN profiling in routine clinical samples to inform microenvironment-directed therapies. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:40220761 | DOI:10.1016/j.cels.2025.101261

Cell Rep Methods. 2025 Apr 8:101026. doi: 10.1016/j.crmeth.2025.101026. Online ahead of print.

ABSTRACT

We present hypergraph variational autoencoder (HyperG-VAE), a Bayesian deep generative model that leverages hypergraph representation to model single-cell RNA sequencing (scRNA-seq) data. The model features a cell encoder with a structural equation model to account for cellular heterogeneity and construct gene regulatory networks (GRNs) alongside a gene encoder using hypergraph self-attention to identify gene modules. The synergistic optimization of encoders via a decoder improves GRN inference, single-cell clustering, and data visualization, as validated by benchmarks. HyperG-VAE effectively uncovers gene regulation patterns and demonstrates robustness in downstream analyses, as shown in B cell development data from bone marrow. Gene set enrichment analysis of overlapping genes in predicted GRNs confirms the gene encoder's role in refining GRN inference. Offering an efficient solution for scRNA-seq analysis and GRN construction, HyperG-VAE also holds the potential for extending GRN modeling to temporal and multimodal single-cell omics.

PMID:40220759 | DOI:10.1016/j.crmeth.2025.101026

Comput Biol Med. 2025 Apr 11;191:110170. doi: 10.1016/j.compbiomed.2025.110170. Online ahead of print.

ABSTRACT

BACKGROUND: Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significant risks for liver fibrosis and cardiovascular complications. Despite extensive research, reliable biomarkers for MASH diagnosis and progression remain elusive. This study aimed to identify hepatic transcriptomic and circulating proteomic signatures specific to MASH, and to develop a machine learning-based biomarker discovery model.

METHODS: A systematic review of RNA-Seq and proteomic datasets was conducted, retrieving 7 hepatic transcriptomics and 3 circulating proteomics studies, encompassing 483 liver samples and 169 serum/plasma samples, respectively. Differential gene and protein expression analyses were performed, and pathways were enriched using gene set enrichment analysis. A machine learning (ML) model was developed to identify MASH-specific biomarkers, utilizing biologically significant protein ratios.

KEY FINDINGS: Hepatic transcriptomic analysis identified 5017 differentially expressed genes (DEGs), with significant enrichment of extracellular matrix (ECM) pathways. Serum proteomics revealed six differentially expressed proteins (DEPs), including complement-related proteins. Integration of transcriptomic and proteomic data highlighted the complement cascade as a key pathway in MASH, with discordant regulation between the liver and circulation. The ML-based biomarker discovery model, utilizing protein ratios, achieved an F1 scores of 0.83 and 0.64 in the training sets and 0.67 in an external validation set.

CONCLUSION: Our findings indicate ECM deregulation and complement system involvement in MASH progression. The novel ML model incorporating protein ratios offers a potential tool for MASH diagnosis. However, further refinement and validation across larger and more diverse cohorts is needed to generalize these results.

PMID:40220593 | DOI:10.1016/j.compbiomed.2025.110170

J Plant Physiol. 2025 Apr 5;308:154487. doi: 10.1016/j.jplph.2025.154487. Online ahead of print.

ABSTRACT

Low-resource environments, such as dry or infertile soils, result in limited plant growth and development, which in turn constrain crop productivity. Water shortage is a significant threat to agricultural productivity all over the world. Drought may also affect plant nutrient uptake and assimilation capability causing nutrient deficiencies even in fertilized fields. Durum wheat is an important staple food crop for ensuring food security in the Mediterranean area, which is increasingly subjected to periods of severe drought due to global changes. Thus, identifying wheat cultivars/genotypes able to cope with suboptimal water, and with unbalanced nutrient availability deriving from drought is crucial to mitigate climate change's adverse effects on agriculture. In this study, a detailed analysis of the phenome, including biomass production, proline production, and characterization of root system architecture, and the ionome, was performed on a panel of 15 Triticum turgidum genotypes, differing for drought tolerance, in order to understand the genotype-specific physiological responses to drought and to identify those genotypes characterised by a positive correlation between ion homeostasis and drought response. The characterization of root system architecture helped our understanding of the morphological responses of wheat plants to drought. Our findings demonstrated that drought exposure for 7 days significantly impacted the ionomic profiles of most genotypes in both shoot and root tissues, albeit to varying degrees. The Lcye A-B- genotype showed the highest accumulation efficiency for most nutrients in shoots, while Bulel tritordeum and Karim in roots. It is also important to understand how micronutrients interact with each other and with macronutrients. Thus, we performed a nutrient correlation network analysis, which showed that drought altered the interactions between nutrients in most genotypes. These findings underscore the importance of understanding the mechanisms regulating nutrient homeostasis, as these mechanisms can either mitigate or exacerbate the impact of drought stress. Understanding the interplay between ionomic profiles and environmental conditions can provide valuable insights into developing more resilient crops that can thrive in challenging environments, ultimately contributing to global food security in the face of climate change.

PMID:40220515 | DOI:10.1016/j.jplph.2025.154487

STAR Protoc. 2025 Apr 11;6(2):103763. doi: 10.1016/j.xpro.2025.103763. Online ahead of print.

ABSTRACT

Noise regulatory proteins are key to understanding the dynamic regulation of cell-to-cell heterogeneity in gene expression. Here, we present a protocol for identifying novel candidate proteins with noise regulatory functions. We describe steps for inhibiting translation in cells, performing single-cell RNA sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and utilizing known regulator-target interactions to integrate obtained data in a regulator enrichment analysis. This protocol has the potential to be applied in any cell line and under culture conditions of choice. For complete details on the use and execution of this protocol, please refer to García-Blay et al.1.

PMID:40220303 | DOI:10.1016/j.xpro.2025.103763

Methods Mol Biol. 2025;2910:105-125. doi: 10.1007/978-1-0716-4446-1_7.

ABSTRACT

The expression pattern of proteins defines the range of biological processes in cellular subcompartments. A core aim in cell biology is therefore to determine the localization and composition of protein complexes within cells. Proximity labeling methodologies offer an unbiased and efficient way to unravel the cellular micro-environment of proteins, providing insights into the molecular networks they participate in. In this chapter, we present a protocol for conducting proximity labeling experiments in primary murine neuronal cultures in vitro based on the proximity-dependent biotinylation identification (BioID) approach. Data acquired through this protocol can be utilized to identify the composition of protein complexes in neurons and to create molecular maps of neuronal subcompartments. This will aid in determining the spatial distribution of biological processes within neurons, and in unraveling fundamental principles of neuronal function and plasticity.

PMID:40220096 | DOI:10.1007/978-1-0716-4446-1_7

Nucleic Acids Res. 2025 Apr 10;53(7):gkaf266. doi: 10.1093/nar/gkaf266.

ABSTRACT

For a few years, it has been possible to experimentally probe the universe of cis and trans RNA-RNA interactions in a transcriptome-wide manner. These experiments give rise to so-called duplex data, i.e. short reads generated via high-throughput sequencing that each encode information on a cis or trans RNA-RNA interaction. These raw duplex data require complex, subsequent computational analyses in order to be interpreted as solid evidence for actual cis and trans RNA-RNA interactions. While several methods have already been proposed to tackle this challenge, almost all of them lack one or more desirable feature-computational efficiency, ability to readily alter the main processing steps and parameter values, p-value estimation for predictions, and interoperability with the common bioinformatics tools for transcriptomics. To overcome these challenges, we present DuplexDiscoverer-a computational method and R package that allows for the efficient, adjustable, and conceptually coherent analysis of duplex data. DuplexDiscoverer is readily adaptable to analysing data from different experimental protocols and its results seamlessly integrate with the most commonly used bioinformatics tools for transcriptomics in R. Most importantly, DuplexDiscoverer generates predictions that are of superior or comparable quality to those of the existing methods while significantly improving time and memory efficiency.

PMID:40219963 | DOI:10.1093/nar/gkaf266

Animals (Basel). 2025 Apr 4;15(7):1045. doi: 10.3390/ani15071045.

ABSTRACT

The frequency, duration and intensity of heat events in Australia are forecast to increase. Different grain types result in different heat loads on animals, so grain selection could reduce the impact of heat exposure. Thirty-two multiparous Holstein cows at 86 days in milk were offered a basal forage diet plus one of four supplements: (1) BLY, rolled barley; (2) CAN, canola meal and rolled wheat; (3) CRN, disk-milled corn; or (4) WHT, rolled wheat. Cows were exposed to a 2-day heat wave in controlled-climate chambers. Overall, cows offered CAN had the lowest dry matter intake (DMI; 16.2 vs. 17.7 kg) but produced more energy-corrected milk (ECM; 34.9 vs. 29.6 kg) when compared with the other treatments. The results were similar during heat exposure. Cows fed CRN and CAN had the greatest body temperature (38.9 °C), and cows fed BLY had the lowest (38.4 °C). Despite this, cows fed BLY had the greatest reduction in DMI from the pre-challenge to the heat-challenge periods (-2.8 vs. -0.4 kg DM/d). There appears to be a small advantage to offering cows a concentrate with a greater protein concentration compared to one that has a greater concentration of fat or starch. The choice of grain to include in a dairy cow's ration during summers with acute heat events may simply be an economic one.

PMID:40218438 | DOI:10.3390/ani15071045

Gut Pathog. 2025 Apr 11;17(1):23. doi: 10.1186/s13099-025-00689-1.

ABSTRACT

BACKGROUND: Breastfeeding is a major determinant of gut microbiota composition and fermentation activity during the first months of life. Breastmilk delivers human milk oligosaccharides (HMO) as substrates for microbial intestinal fermentation. One of the main metabolites that accumulates in feces of breastfed infants is 1,2-propanediol (1,2PD) resulting from the metabolism of fucosylated HMO. 1,2PD is used in microbial cross-feeding to produce propionate, but 1,2PD is also an alcohol that can impact the state of the microbial cell envelope. To shed further light on an understudied compound in the infant gut, we investigated the genetic and metabolic potential of the early gut colonizer Clostridium perfringens to utilise 1,2PD, and the interactions of 1,2PD with the cell envelope.

RESULTS: Based on genome analysis, C. perfringens FMT 1006 isolated from infant feces possessed most genes of the pdu operon related to 1,2PD metabolism. C. perfringens consumed 1,2PD (78%) and produced 1-propanol as the main metabolite, while propionate was not detected. In agreement, genes responsible for 1,2PD utilisation and propanol formation (pduCDE, dhaT) were highly expressed. When cultivated in the presence of 1,2PD and glucose, a higher proportion of 1,2PD carbon (87%) was recovered as compared to incubation with only 1,2PD (34%). At the same time, lactate and acetate were formed in a ratio of 2.16:1.0 with 1,2PD and glucose compared to a ratio 9.0:1.0 during growth with only glucose possibly due to reallocation of the NAD+/NADH pool in favor of 1-propanol formation. The presence of 1,2PD slightly increased membrane fluidity and modified the composition of the membrane to a higher content of elongated glycerophosphoethanolamines.

CONCLUSION: We provide here new knowledge on the metabolism of 1,2PD by a microbial species that is present during breastfeeding and observed that C. perfringens metabolised 1,2PD mainly to propanol. The presence of 1,2PD had little impact on membrane fluidity and let to modifications of membrane lipid composition. Collectively, these findings advance our understanding of on intestinal metabolite-microbe interactions during breastfeeding.

PMID:40217307 | DOI:10.1186/s13099-025-00689-1

Nat Microbiol. 2025 Apr 11. doi: 10.1038/s41564-025-01972-2. Online ahead of print.

ABSTRACT

Metabolic models of individual microorganisms or small microbial consortia have become standard research tools in the bioengineering and systems biology fields. However, extending metabolic modelling to diverse microbial communities, such as those in the human gut, remains a practical challenge from both modelling and experimental validation perspectives. In complex communities, metabolic models accounting for community dynamics, or those that consider multiple objectives, may provide optimal predictions over simpler steady-state models, but require a much higher computational cost. Here we describe some of the strengths and limitations of microbial community-scale metabolic models and argue for a robust validation framework for developing personalized, mechanistic and accurate predictions of microbial community metabolic behaviours across environmental contexts. Ultimately, quantitatively accurate microbial community-scale metabolic models could aid in the design and testing of personalized prebiotic, probiotic and dietary interventions that optimize for translationally relevant outcomes.

PMID:40217129 | DOI:10.1038/s41564-025-01972-2

Nat Med. 2025 Apr 11. doi: 10.1038/s41591-025-03615-9. Online ahead of print.

ABSTRACT

The landscape of clinical microbiome research has dramatically evolved over the past decade. By leveraging in vivo and in vitro experimentation, multiomic approaches and computational biology, we have uncovered mechanisms of action and microbial metrics of association and identified effective ways to modify the microbiome in many diseases and treatment modalities. This Review explores recent advances in the clinical application of microbiome research over the past 5 years, while acknowledging existing barriers and highlighting opportunities. We focus on the translation of microbiome research into clinical practice, spearheaded by Food and Drug Administration (FDA)-approved microbiome therapies for recurrent Clostridioides difficile infections and the emerging fields of microbiome-based diagnostics and therapeutics. We highlight key examples of studies demonstrating how microbiome mechanisms, metrics and modifiers can advance clinical practice. We also discuss forward-looking perspectives on key challenges and opportunities toward integrating microbiome data into routine clinical practice, precision medicine and personalized healthcare and nutrition.

PMID:40217076 | DOI:10.1038/s41591-025-03615-9

Oncogene. 2025 Apr 11. doi: 10.1038/s41388-025-03362-8. Online ahead of print.

NO ABSTRACT

PMID:40216970 | DOI:10.1038/s41388-025-03362-8

Commun Chem. 2025 Apr 11;8(1):114. doi: 10.1038/s42004-025-01484-4.

ABSTRACT

Computational techniques for predicting molecular properties are emerging as key components for streamlining drug development, optimizing time and financial investments. Here, we introduce ChemLM, a transformer language model for this task. ChemLM leverages self-supervised domain adaptation on chemical molecules to enhance its predictive performance. Within the framework of ChemLM, chemical compounds are conceptualized as sentences composed of distinct chemical 'words', which are employed for training a specialized chemical language model. On the standard benchmark datasets, ChemLM either matched or surpassed the performance of current state-of-the-art methods. Furthermore, we evaluated the effectiveness of ChemLM in identifying highly potent pathoblockers targeting Pseudomonas aeruginosa (PA), a pathogen that has shown an increased prevalence of multidrug-resistant strains and has been identified as a critical priority for the development of new medications. ChemLM demonstrated substantially higher accuracy in identifying highly potent pathoblockers against PA when compared to state-of-the-art approaches. An intrinsic evaluation demonstrated the consistency of the chemical language model's representation concerning chemical properties. The results from benchmarking, experimental data and intrinsic analysis of the ChemLM space confirm the wide applicability of ChemLM for enhancing molecular property prediction within the chemical domain.

PMID:40216964 | DOI:10.1038/s42004-025-01484-4

Metabolomics. 2025 Apr 11;21(3):51. doi: 10.1007/s11306-025-02251-1.

NO ABSTRACT

PMID:40216608 | DOI:10.1007/s11306-025-02251-1

Clin Microbiol Infect. 2025 Apr 9:S1198-743X(25)00163-6. doi: 10.1016/j.cmi.2025.04.003. Online ahead of print.

ABSTRACT

BACKGROUND: Candida auris is a multidrug-resistant yeast that emerged as a significant healthcare-associated pathogen. It is classified as an urgent threat to public health due to the high resistance to available antifungal agents. Globally six distinct clades of C. auris have been identified with varying antifungal susceptibility patterns and geographical distributions.

OBJECTIVES: The aim of this review is investigating the (published) antifungal susceptibility profiles of different C. auris clades to identify those with a higher prevalence of resistance.

SOURCES: A comprehensive literature review was conducted using PubMed, SciELO, Google Scholar, and MEDLINE databases to collect data on Minimum Inhibitory Concentration (MIC) distributions and clade designations of C. auris strains.

CONTENT: A total of 1,031 C. auris strains were included. Clades I and III, which are closely related phylogenetically, displayed the highest resistance rates, particularly to fluconazole, with 94% and 96% of isolates, respectively. Clade IV also exhibited resistance to both azoles and echinocandins. In contrast, Clades II, V, and VI had lower resistance rates, with Clade VI being entirely susceptible to fluconazole. Anidulafungin demonstrated the greatest efficacy across all clades, with resistance rates ranging from 0% to 3.67%. Furthermore, Clades V and VI showed complete susceptibility to all antifungal agents evaluated.

IMPLICATIONS: This study highlights significant variations in antifungal resistance profiles across the six C. auris clades. Clades I, III, and IV stand out due to their multidrug resistance, particularly to fluconazole and amphotericin B, posing serious challenges for treatment. Continuous global surveillance and tailored management strategies are essential for controlling C. auris infections, especially in highly resistant clades. Enhanced diagnostic capabilities and further genomic studies are critical to understanding the evolving nature of resistance in this emerging pathogen and improving therapeutic outcomes. Clade-specific antifungal resistance in C. auris requires monitoring to optimize therapy selection during outbreaks.

PMID:40216246 | DOI:10.1016/j.cmi.2025.04.003