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PubMed2026

Zebrafish as a versatile model in biomedical research, from disease modeling to regenerative medicine: a review.

Zebrafish are an effective animal model widely utilized in biomedical research. They are known for their rapid reproduction and substantial genetic similarity to humans. Their transparent embryos directly enable the visualization of developmental processes and disease progression. This makes zebrafish invaluable for studying a broad range of human diseases, including cancer, cardiovascular disorders, and neurodegenerative conditions. Compared with other vertebrate models, zebrafish offer several advantages, including ease of genome editing, cost-effective maintenance, and suitability for high-throughput drug screening. Recent advancements have expanded the use of zebrafish in disease modeling and regenerative medicine, providing deeper insights into the genetic and cellular mechanisms underlying human pathologies. Zebrafish provide a robust platform for evaluating the safety, efficacy, and regenerative potential of both natural and synthetic biomaterials, including hydroxyapatite, bioactive glass nanoparticles, and bioceramics. This capability facilitates the creation of artificial tissues that closely resemble native structures. Additionally, integrating artificial intelligence technologies has improved automated data analysis and phenotyping in zebrafish studies, enhancing both accuracy and throughput. This review highlights current applications of zebrafish in disease modeling, drug discovery, regenerative medicine, and biomaterial assessment, emphasizing their evolving role as a versatile preclinical platform supported by advanced genetic and computational tools.

Animals↗
PubMed2026

Association Between cnm-Positive Streptococci and Cerebral Small Vessel Disease: Insights From Oral Health and Microbiome Status.

INTRODUCTION AND AIMS: Cerebral small vessel disease (CSVD) is associated with various severe neurological outcomes; while oral cnm-positive streptococci are suggested to be involved in cerebrovascular lesions, the specific associative features between these bacteria and CSVD have not yet been systematically investigated. This study aims to investigate the prevalence of cnm-positive streptococci in patients with CSVD and explore the correlation between infection and CSVD severity. By integrating oral health indices and microbiome sequencing, we evaluate the oral hygiene status and microbial dysbiosis characteristics of cnm-positive streptococci carriers. Furthermore, cnm-positive streptococci derived from CSVD patients will be isolated, identified, and subjected to whole-genome sequencing to provide a foundation for future research. METHODS: To explore cnm-positive streptococci prevalence and its association with CSVD, we conducted a case-control study comparing their oral detection rates between healthy controls and CSVD patients. We also performed 16S rRNA gene high-throughput sequencing of oral plaque microbiota and assessed oral health, including the simplified oral hygiene index (OHI-S), the decayed, missing, and filled teeth (DMFT) index, oral hygiene practices, gingival status, and saliva scores. RESULTS: cnm-positive streptococci were more prevalent in CSVD patients, correlating with higher OHI-S and microbial dysbiosis. Multivariable regression models (adjusted for demographic/vascular risk factors) linked cnm positivity to periventricular hyperintensities (PVH), deep white matter hyperintensities (DWMH), Fazekas score, and total CSVD burden (not cerebral microbleeds (CMBs)/lacunes). CONCLUSION: Oral cnm-positive streptococci are independently associated with CSVD phenotypes, particularly those characterized by white matter injury. These findings presents a potential oral-cerebrovascular interaction and imply that managing specific virulent oral strains may be a noteworthy consideration in future clinical research.

Humans↗
PubMed2026

Comprehensive profiling of antibiotic resistance genes and functional clusters of orthologous groups annotation of gut microbiota in Indonesian Kedu chickens.

Antibiotic resistance is a growing global health concern, with poultry systems acting as important reservoirs of antibiotic resistance genes (ARGs). However, resistome and functional profiles of indigenous chickens raised under traditional systems remain underexplored. This study aimed to characterize the antibiotic resistome, virulence factor genes, and metabolic potential of gut microbiota in Indonesian Kedu chickens using a shotgun metagenomic approach. Digesta samples from five gastrointestinal segments of 21 healthy adult chickens were analyzed through high-throughput sequencing. ARGs were identified using the Comprehensive Antibiotic Resistance Database (CARD) and Antibiotic Resistance Genes Databases (ARDB), while virulence factors and functional genes were annotated using Virulence Factor Database (VFDB), Clusters of Orthologous Groups (COG), and Carbohydrate-Active EnZymes (CAZy) databases. Results revealed a diverse resistome dominated by multidrug resistance and efflux pump mechanisms, with prominent genes associated with fluoroquinolone, tetracycline, β-lactam, and glycopeptide resistance. The detection of clinically relevant ARGs suggests that genetic determinants associated with antimicrobial resistance are present in the gut microbiota of traditionally raised Kedu chickens, although metagenomic data alone cannot determine whether these genes are actively expressed or confer phenotypic resistance. Virulence factor analysis showed functions related to adherence, immune evasion, iron acquisition, quorum sensing, and efflux activity, reflecting strong microbial adaptability. Functional profiling demonstrated enrichment in translation, carbohydrate and amino acid metabolism, genome maintenance, and cell envelope biogenesis. Additionally, CAZyme analysis indicated a high capacity for complex polysaccharide degradation, supporting efficient utilization of fiber-rich traditional diets. In conclusion, this study provides a comprehensive metagenomic overview of antibiotic resistance and functional potential in Kedu chicken gut microbiota, emphasizing the importance of incorporating indigenous poultry into antimicrobial resistance surveillance within a One Health framework.

Antibiotic resistance genes↗
PubMed2026

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture↗
PubMed2025

Missense variants pathogenicity annotation from homologous proteins.

MOTIVATION: High-throughput DNA sequencing has revealed millions of single nucleotide variants (SNVs) in the human genome, with a small fraction linked to disease. The effect of missense variants, which alter the protein sequence, is particularly challenging to interpret due to the scarcity of clinical annotations and experimental information. While using conservation and structural information, current prediction tools still struggle to predict variant pathogenicity. In this study, we explored the pathogenicity of homologous missense variants-variants in equivalent positions across homologous proteins-focusing on proteins involved in autosomal dominant diseases. RESULTS: Our analysis of 2976 pathogenic and 17 555 non-pathogenic homologous variants demonstrated that pathogenicity can be extrapolated with 95% accuracy within a family, or up to 98% for closer homologs. Remarkably, the evaluation of 27 commonly used mutation predictor methods revealed that they were not fully capturing this biological feature. To facilitate the exploration of homologous variants, we created HomolVar, a web server that computationally predicts the pathogenesis of missense variants using annotations from homologous variants, freely available at https://rarevariants.org/HomolVar. Overall, these findings and the accompanying tool offer a robust method for predicting the pathogenicity of unannotated variants, enhancing genotype-phenotype correlations, and contributing to diagnosing rare genetic disorders. AVAILABILITY AND IMPLEMENTATION: HomolVar is freely available at https://rarevariants.org/HomolVar.

Mutation, Missense↗
PubMed2026

From the Microscope to the Genome: A New Era in the Molecular Genetics of Epidermolysis Bullosa.

Epidermolysis bullosa (EB) is a heterogeneous group of inherited disorders characterised by skin fragility, caused by pathogenic variants in genes encoding structural components of the dermo-epidermal junction. With the advent of next-generation sequencing (NGS), the diagnostic paradigm has shifted from a morphological to a genotype-oriented approach. This review summarises the genetic architecture of EB, the types of mutations and genotype-phenotype relationships, the challenges in interpreting variants of unknown significance (VUS), and therapeutic strategies targeting specific mutational mechanisms, including read-through approaches, exon skipping and genome editing. The role of modifier genes and epigenetic factors in clinical variability is also discussed. The focus is on the translational potential of genomics for personalized therapy in EB. Overall, this review synthesizes the molecular basis of all four major EB types across 16+ classical genes, highlights the paradigm shift where NGS achieves a diagnostic yield exceeding 90%, and critically assesses recent therapeutic milestones-ranging from the first FDA-approved topical gene therapy to precision RNA and genome-editing modalities.

Humans↗
PubMed2026

needLR: long-read structural variant annotation with population-scale frequency estimation.

SUMMARY: We present needLR, a structural variant (SV) annotation tool that can be used for filtering and prioritization of candidate pathogenic SVs from long-read sequencing data using population allele frequencies, annotations for genomic context, and gene-phenotype associations. When using population data from 500 presumably healthy individuals to evaluate nine test cases with known pathogenic SVs, needLR assigned allele frequencies to over 97.5% of all detected SVs and reduced the average number of novel genic SVs to 121 per case while retaining all known pathogenic variants. AVAILABILITY AND IMPLEMENTATION: needLR is implemented in bash with dependencies including Truvari v4.2.2, BEDTools v2.31.1, and BCFtools v1.19. Source code, documentation, and pre-computed population allele frequency data are freely available at https://github.com/jgust1/needLR under an MIT license and archived on Zenodo at https://zenodo.org/records/19463479.

Software↗
PubMed2026

DURABLE: A Workflow for Determining Corrosion-Driving and Protective Microbial Mechanisms.

Microbiologically influenced corrosion (MIC) threatens global infrastructure, causing billions of dollars in annual losses. Its persistence stems from unresolved mechanisms─particularly the metabolites produced by microorganisms that drive or inhibit corrosion─and the microbial community structures. Progress has been hindered by the absence of systematic workflows to rapidly and accurately identify MIC-relevant microorganisms and their functions. Here, we present DURABLE (Detection of Unique Corrosion Resistant or Accelerating Biologics in a Laboratory Environment), a pipeline that couples high-throughput microbial screening with genomic and metabolic workflows. We applied the DURABLE workflow to six diesel tank samples and revealed fuel-dependent microbial community structures, which showed greater diversity and evenness in bacterial communities than their fungal counterparts. The workflow used carbon steel beads to rapidly screen over 80 bacterial isolates for corrosive activity, reducing assay time to approximately 2 days compared with the conventional 30-day metal coupon test. More than 40 isolates were identified as corrosive. Further testing using mass spectrometry analysis revealed corrosion-associated metabolites, which were further validated using electrochemical assays. Thus, DURABLE achieved a ∼15-fold increase in screening speed and provided a scalable and mechanistic framework for dissecting MIC dynamics. We expect this advance will enable the development of precision mitigation strategies in hydrocarbon fuel infrastructure.

Bacteria↗
PubMed2025

Identification of intragenic variants in pediatric patients with intellectual disability in Peru.

BACKGROUND: Intellectual disability in Latin America can reach a frequency of 12% of the population, these may include nutritional deficiencies, exposure to toxic or infectious agents, and the lack of universal neonatal screening programs. In 90% of patients with intellectual disability, the etiology can be attributed to variants in the genome. OBJECTIVE: to determine intragenic variants in patients with intellectual disability between 5 and 18 years old at Instituto Nacional de Salud del Niño. METHODS: It is a descriptive cross-sectional study with convenience sampling. A total of 124 children diagnosed with intellectual disability were selected based on psychological test results and availability for whole exome sequencing. In addition, a chromosomal analysis of 6.55 M was performed on ten patients with a negative result in sequencing. Relative and absolute frequencies and measures of central tendency and dispersion were determined according to their nature. In addition, multiple linear regression and Poisson regression were used to determine the association between some clinical characteristics and the probability of occurrence in patients with positive results. RESULTS: The median age of the patients was 6.3 (IQR = 5.95), males accounted for 57.3%, and 91.9% of the cases had mild intellectual disability. Exome sequencing determined the etiology in 30.6% of patients with intellectual disability, of which 52.6% were autosomal dominant inheritance. The most frequent genes found were MECP2, STXBP1 and LAMA2. A broad genotype-phenotype correlation was identified, highlighting the genetic heterogeneity of intellectual disability in this population. The presence of dermatologic lesions, dystonia, peripheral neurological disorders, and fourth finger flexion limitation were observed more frequently in patients with intellectual disability with "positive results". CONCLUSIONS: This study shows that one-third of patients with intellectual disability exhibit intragenic variants, highlighting the importance of genetic analysis for accurate diagnosis. The identification of genes such as MECP2, STXBP1, and LAMA2 underscores the genetic heterogeneity of intellectual disability in the studied population. These findings emphasize the need for genetic testing in clinical management and the implementation of early detection programs in Peru.

Humans↗
PubMed2026

Integrating Next-Generation Sequencing into von Willebrand Disease Diagnostics: Insights from the PCM-EVW-ES Multicenter Project.

Von Willebrand disease (VWD) is the most common inherited bleeding disorder, caused by quantitative or qualitative defects in von Willebrand factor (VWF). Diagnosis is challenging and requires integrating bleeding history, VWF antigen and activity measurements, FVIII assays, and specialized phenotyping. Genetic testing is increasingly recognized as a key component. Here, we review current concepts in VWD diagnostics and highlight the Spanish Clinical and Molecular Profile of von Willebrand Disease (PCM-EVW-ES) project as a model for genomics-enabled precision medicine. PCM-EVW-ES is a multicenter initiative involving 48 hospitals, centralized phenotypic testing, and next-generation sequencing of the VWF coding region, enabling definitive classification in 730 individuals with VWD to date. Harmonized recruitment criteria and standardized workflows improve subtype assignment, uncover complex genotypes, refine genotype-phenotype correlations, and facilitate the identification of asymptomatic carriers. The PCM-EVW-ES variant spectrum highlights recurrent disease-causing variants in Spain and underscores the value of coordinated national registries for variant curation. Building on these data, we propose a diagnostic algorithm in which bleeding assessment and first-line VWF/FVIII assays, combined with, early VWF molecular testing increases diagnostic accuracy and guides targeted second-line investigations to confirm and refine VWD subtype classification. We also outline persisting challenges, including the interpretation of variants of uncertain significance and patients without identifiable pathogenic VWF variants, and future directions integrating third-generation sequencing, expanded gene panels, functional studies, and artificial-intelligence-driven multiomic approaches. Together, these advances illustrate how robust multicenter studies can bridge the gap between complex diagnostics and clinical practice in VWD.

Humans↗
PubMed2026

The diagnostic potential of combined quantitative polymerase chain reaction and next-generation sequencing using the same primers for periprosthetic joint infection.

Next-generation sequencing (NGS) enables the detection of specific pathogens unidentifiable by conventional cultures, but its application in orthopedics remains inconsistent due to background contamination and irreproducible findings. This study evaluated the diagnostic performance of a novel workflow combining broad-range 16S rRNA gene quantitative PCR (qPCR) screening with downstream NGS, focusing on bacterial biomass thresholds. The qPCR assay demonstrated excellent intrarater reliability, with an intraclass correlation coefficient (ICC) of 0.961 (95% confidence interval, 0.881 to 0.997). Based on serially diluted positive controls, a quantitative threshold of 10⁵ CFU/mL was established as the minimum concentration required for the consistent detection of fastidious taxa, such as Escherichia coli. When evaluated against conventional cultures using 95 sonicate fluid and 276 pre/intraoperative tissue samples, the qPCR assay achieved a sensitivity of 80% and a specificity of 72%. Subsequent NGS sequencing of 26 clinical samples and 9 controls showed concordance in 4 of 6 culture-positive infected cases with NGS taxonomy, whereas the remaining discrepancies were likely attributable to culture-based phenotypic misidentification. Notably, among the qPCR-positive cases, three were culture-negative, including two hip prosthesis loosening cases exhibiting polymicrobial profiles, and one post-traumatic osteoarthritis case harboring low-level Staphylococcus. Crucially, this post-traumatic patient developed delayed periprosthetic joint infection (PJI) 2 years post-surgery, with cultures identifying Staphylococcus previously detected by the initial NGS analysis. Integrating qPCR screening with targeted NGS effectively refines pathogen identification, filters environmental artifacts, and overcomes the diagnostic limitations of culture-negative infections in orthopedic practice.IMPORTANCENext-generation sequencing (NGS) enables the detection of specific pathogens in clinical samples that are not identifiable by conventional methods. However, NGS applications in orthopedics have not been quantitatively evaluated, and findings have been inconsistent owing to contaminants and the presence of non-credible causative organisms. These factors primarily stem from the failure to evaluate low-biomass samples and the absence of proper controls, such as negative controls or mock community DNA samples. This study demonstrates that interpreting results from low-biomass samples requires careful consideration because NGS relies on relative bacterial abundances; distinguishing likely pathogens from contaminants is particularly challenging when bacterial loads are low. We demonstrated that combining NGS with quantitative PCR (qPCR) and applying a Cq cutoff can reduce false positives.

Humans↗
PubMed2026

Next-generation newborn screening: feasibility of combined genetic and biochemical testing for 95 treatable inherited metabolic disorders.

INTRODUCTION: Next-generation sequencing (NGS) is gaining attention in newborn screening (NBS) for its ability to detect treatable genetic disorders, especially those without a biochemical footprint. However, NGS-NBS requires interpreting variants without phenotype information or family trio analysis. Biochemical tests, preferably in dried blood spots (DBS), are therefore useful to confirm the pathogenicity of variants identified by NGS-NBS and increase its specificity and sensitivity. OBJECTIVES: We aimed to explore the potential of combined genetic-biochemical testing for 95 treatable Inherited Metabolic Disorders (IMD) considered eligible for NGS-NBS (100 genes) previously identified by our research group. METHODS: We reviewed the Collaborative Laboratory Integrated Reports (CLIR) and carried out systematic literature reviews in PubMed and Embase to identify biochemical tests for 95 IMD. Biochemical tests conducted on DBS were differentiated from tests that require referral. RESULTS: We identified DBS-biochemical tests for 72 of the 95 IMD (77/100 genes). DBS-based biochemical tests for 55 IMD (60 genes) are already implemented in NBS. For the other 23 IMD, biochemical tests in non-DBS specimens are reported, although some are less sensitive when measured at neonatal age in presymptomatic infants. CONCLUSION: We present a comprehensive overview of current biochemical tests for 95 IMD. These tests can be used to confirm inconclusive NGS-NBS results, and combined genetic-biochemical testing is expected to improve both the negative and positive predictive values of NBS programs.

Humans↗
PubMed2026

Obtaining a Diagnostic Yield via Scan findings prior to the introduction of SEquencing retrospectivelY (ODYSSEY): a cohort study.

OBJECTIVE: To determine the retrospective yield of prenatal exome sequencing (PES) by establishing the proportion of children with a postnatal monogenic diagnosis that could have been diagnosed prenatally if PES had been available. METHODS: The study cohort comprised a sample of children in Northern Ireland, born between January 2010 and January 2018 (predating routine availability of PES), who received a monogenic diagnosis postnatally via next generation sequencing as part of either of two UK-wide studies (the 100 000 Genomes Project (2015-2018) or the Deciphering Developmental Disorders study (2011-2015)). Clinical data were collected retrospectively and correlated with the current UK National Health Service PES protocol, including the phenotypic eligibility criteria for PES and the associated fetal anomalies gene panel. Cases were considered retrospective diagnoses if the fetal phenotype would have been eligible for PES and the diagnostic gene was included on the test panel, meaning prenatal diagnosis in this current era could have been feasible. RESULTS: Of 101 children, 17.8% (95% CI, 10.3-25.3%) had both an eligible fetal structural anomaly (FSA) (i.e. high-risk FSA) and a diagnostic gene on the associated test panel, meaning that they could have been diagnosed prenatally in the current clinical landscape. The median length of the diagnostic odyssey for this subgroup of children was 3.7 years (1354 (range, 822-2450) days). Moreover, 58.4% (n = 59) of cases had no anomalies detected prenatally and 19.8% (n = 20) had a FSA that would not meet the eligibility criteria for PES (low-risk FSA). Although these cases would have been ineligible for PES under the current clinical pathway, 89.9% (n = 71/79) were affected by severe or profound syndromes. Postnatally, the most common functional anomalies were neurodevelopmental delay/intellectual disability and/or behavioral abnormality, which were observed in 80.2% (n = 81) of the included children. However, 80.2% (n = 65/81) of these affected children did not present with fetal anomalies eligible for PES. CONCLUSIONS: Almost one-fifth of children with a monogenic condition included in this study could have received a diagnosis via modern PES, avoiding a diagnostic odyssey lasting almost 4 years. However, despite having a monogenic condition, over half of the children did not present with any structural anomalies in utero. This demonstrates the degree to which fetal imaging is limited in its ability to reassure parents of the absence of a fetal genetic syndrome. © 2026 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Humans↗
PubMed2026

Integrating histology and spatial transcriptomics via multimodal transformers and contrastive representation learning for accurate gene expression prediction.

Predicting spatial gene expression from Histological images is a fundamental task in understanding tissue organization and molecular phenotypes. However, existing methods often rely on single-model representations or lack effective alignment between image and transcriptomic features. To address these limitations, we propose a unified multimodal learning framework that integrates histological imaging and spatial transcriptomics through a shared latent representation space. Specifically, histological H&E images are encoded by a ResNet50-based convolutional stem and a MobileViT Transformer backbone to extract hierarchical visual representations. Both modalities are projected into a shared latent space via linear-GELU-dropout transformation blocks, enabling cross-modal alignment through a contrastive learning objective that maximizes agreement between the corresponding image and the spot embeddings. Experimental results on the 10x Genomics Visium dataset of human liver tissue demonstrate that MViTGene achieves significantly higher prediction accuracy than existing methods across multiple gene subsets, with improvements of 20%, 33%, and 12% in predicting marker genes, highly expressed genes, and highly variable genes, respectively. The significant improvement in relevance indicates that the model can more accurately capture the true correspondence between tissue morphology and gene expression, therefore enabling more reliable biological interpretation. It provides a computational tool for high-throughput spatial gene expression prediction that balances performance and interpretability.

Humans↗
PubMed2026

Characterisation of Bordetella pertussis virulence and macrolide resistance in Australia by targeted culture-independent sequencing: a genomic epidemiology study.

BACKGROUND: Bordetella pertussis continues to circulate globally despite widespread vaccination, with a notable epidemic in 2024. Its resurgence is confounded by the emergence of pertactin-deficient, macrolide-resistant B pertussis strains in Asia and Europe, which are under-recognised by conventional diagnostics. We aimed to apply targeted culture-independent next-generation sequencing (tNGS) of respiratory specimens to improve global B pertussis diagnostic capability and genomic surveillance. METHODS: We did a nationwide genomic epidemiology study of B pertussis RT-PCR-positive respiratory specimens that were retrospectively and prospectively collected by diagnostic and public health laboratories in six of seven states and territories of Australia. Specimens underwent tNGS and macrolide-resistant B pertussis-specific PCR, and an opportunistic subset from New South Wales and Queensland were cultured for confirmatory susceptibility testing and whole-genome sequencing. Sequencing data were analysed for genome recovery, virulence profiles, and macrolide resistance mutations, and were compared with international macrolide-resistant B pertussis genomes and ancestral Australian genomes. The performance of the tNGS approach was assessed with logistic regression relative to RT-PCR cycle threshold values, and sensitivity and specificity values were calculated. FINDINGS: 255 respiratory specimens positive for B pertussis were included in the study. 64 (25%) were retrospectively collected between Jan 12, 2012, and Dec 31, 2023, and 191 (75%) were prospectively collected between Jan 1 and Oct 28, 2024. Of these 255 specimens, 148 (58%) yielded near-complete B pertussis genomes through tNGS. Seven co-circulating lineages of B pertussis were documented, including two associated with macrolide-resistance. Eight epidemiologically unrelated and geographically dispersed cases of macrolide-resistant B pertussis with a 23S rRNA 2037A→G mutation were identified by tNGS and confirmed by whole-genome sequencing. Three of these were further validated by phenotypic testing. The estimated prevalence of macrolide resistance among Australian cases positive for B pertussis was 4% (eight of 188). INTERPRETATION: tNGS can recover near-complete B pertussis genomes directly from clinical specimens, enabling identification of macrolide resistance mutations and high-resolution phylogenetic analysis. These findings show that tNGS complements PCR-based surveillance by providing genome-wide assessment of resistance, virulence, and genomic diversity in a single workflow. FUNDING: NSW Health Prevention Research Support Program.

Macrolides↗
PubMed2026

Deep tissue sequencing improves genetic diagnostic yield in focal cortical dysplasia.

Focal cortical dysplasias (FCDs) are malformations of cortical development associated with drug-resistant focal epilepsy. We analyzed surgical tissue from 25 consecutive cases recruited from adult and pediatric epilepsy surgery programs. We performed high-depth sequencing of lesional tissue, validated somatic variants using droplet digital PCR or amplicon sequencing, and investigated genotype-phenotype correlations. A pathogenic or likely pathogenic variant was detected in 64% (n = 16/25) of cases. Of these, five cases with FCDIIa or FCDIIb had germline variants in NPRL3 (n = 3) or DEPDC5 (n = 2). Somatic variants were identified in 44% (n = 11/25) of cases. The genetic yield for FCDIIb was 77% of cases having a pathogenic mTOR pathway variant detected (n = 10/13), and for FCDIIa 66% (n = 6/9). High depth sequencing approaches allowed detection of somatic variants with very low (down to 0.4%) variant allele fractions (VAFs). No pathogenic variants were detected in 3 cases with FCDI. 62% (n = 15/24) of the cases with ≥12 months follow up experienced a favourable seizure outcome (Engel 1-2) following surgery. Of note, n = 9 patients required repeat surgery to resect residual dysplasia. Determining a genetic diagnosis reveals aetiology and paves the way to precision therapies that may benefit those with FCD who do not respond to current treatments.

Humans↗
PubMed2026

Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.

Tumor heterogeneity-encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts-is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-cell resolution. Here, we review the technical foundations required for high-quality scRNA-seq studies. We then trace the evolution of scRNA-seq platforms from manual micromanipulation to high-throughput systems, and describe the computational pipelines that enable reliable data interpretation. The application of scRNA-seq is exemplarily shown in the context of lung cancer, where single-cell profiling has revealed (i) the clonal and sub-clonal architecture of tumors, (ii) extensive remodeling of the immune microenvironment, iii) key mechanisms underlying resistance to targeted agents and immune-checkpoint blockade, and (iv) the dynamics of neo-antigen-specific T-cell responses. Integrating machine-learning techniques-such as deep-learning classifiers and graph-based models-with single-cell transcriptomic data has markedly sped up biomarker discovery, produced more accurate risk-stratification scores, and enabled the generation of patient-specific therapeutic predictions. We surveyed the major trial registry ClinicalTrials.gov and identified ∼380 ongoing or completed studies that explicitly incorporate scRNA-seq as a correlative or pharmacodynamic endpoint. Overall, the analysis shows that scRNA-seq becomes an increasingly important component of modern trials, providing high-resolution cellular and molecular readouts that complement conventional imaging and bulk-omics endpoints. While key challenges remain, ranging from costs, scalability and need for rigorous validation before routine clinical deployment, ongoing technological advances continue to expand the potential of scRNA-seq as a cornerstone of precision medicine.

Humans↗
PubMed2026

Performance of the IR Biotyper, Nanopore, and Illumina sequencing to discriminate Escherichia coli strains originating from poultry.

UNLABELLED: Escherichia coli is a highly diverse bacterial species that includes avian pathogenic E. coli (APEC), one of the most prevalent causative agents of disease in poultry worldwide. Rapid and accurate discrimination of E. coli strains is essential for outbreak management, antimicrobial resistance surveillance, and vaccine development. In this study, we compared the performance of Fourier Transform Infrared (FTIR) spectroscopy using the IR Biotyper system with Nanopore and Illumina whole-genome sequencing (WGS) for typing 200 E. coli isolates, originating from four poultry rearing farms in the Netherlands. From each farm, we sampled 10 one-day-old meat type rearing chicks, and from every chick, we isolated 5 E. coli strains. FTIR clustering showed strong concordance with WGS-based classifications, particularly serotyping and core-genome similarity determined by PopPUNK analysis (Adjusted Rand Index 0.75-0.92). While Nanopore and Illumina sequencing provided the highest genetic resolution, FTIR offered a faster (max 6 vs 12-28 days for 200 isolates) and more cost-effective alternative for assessing clonality. Across all methods, multiple strains were detected per farm, whereas most birds carried a single dominant E. coli strain. Our findings demonstrate that FTIR provides a reliable and scalable phenotypic method for rapid strain discrimination in E. coli, complementing WGS in diagnostic, surveillance, and epidemiological settings where speed and throughput are critical. IMPORTANCE: Escherichia coli is a major pathogen in poultry and a potential zoonotic risk for humans. Rapid and accurate discrimination of avian pathogenic E. coli (APEC) strains is critical for outbreak management, antimicrobial resistance surveillance, and the design of effective autogenous vaccines. In this study, we compared Fourier Transform Infrared (FTIR) spectroscopy with Nanopore and Illumina whole-genome sequencing for strain typing of E. coli isolates originating from poultry. The results show that FTIR provides comparable clustering accuracy to genomic approaches at a fraction of the time and costs. This work demonstrates that FTIR can serve as a practical, high-throughput alternative for routine monitoring of E. coli in veterinary diagnostics and food safety of poultry meat, enabling faster decision-making and more targeted interventions across the poultry production chain.

Animals↗