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PubMed2025

SPARKI: a tool for the statistical analysis of pathogen identification results.

MOTIVATION: Many pathogen identification and microbiome analysis tools have been developed in recent years, with Kraken 2 being one of the most popular. While tools downstream of Kraken 2 can assist in the interpretation of its outputs, a statistical framework to assess the likelihood that a taxon/organism is present in a single sample alongside an automated end-to-end analysis pipeline has not yet been fully implemented. RESULTS: Here, we introduce SPARKI, an R package that performs statistical analysis of Kraken 2 outputs and aids in the identification of pathogens present in next-generation sequencing samples. SPARKI adds to the field by bringing a probabilistic view to Kraken 2 data, serving as a discovery tool and complementing other methods such as KrakenTools, Bracken, and Pavian. AVAILABILITY AND IMPLEMENTATION: SPARKI code is available on GitHub at https://github.com/team113sanger/sparki. SPARKI is also part of an end-to-end pathogen identification pipeline, sparki-nf, which is available at https://github.com/team113sanger/sparki-nf. An additional pipeline for further exploration and validation of SPARKI results is also available at https://github.com/team113sanger/map-to-genome.

Software↗

PubMed2026

Clinical impact of metagenomic next-generation sequencing for pathogen identification and guided therapy in pediatric intensive care unit patients with severe pulmonary infections.

UNLABELLED: To explore the diagnostic efficiency, clinical concordance, and precision treatment value of metagenomic next-generation sequencing (mNGS) for severe pulmonary infections in children in the pediatric intensive care unit (PICU), and to provide evidence for improving microbiological diagnosis and optimizing anti-infective strategies. A retrospective cohort study included 89 children with severe pneumonia in the PICU in 2024. All underwent routine microbiological testing and mNGS of bronchoalveolar lavage fluid (BALF). Detection rates, pathogen composition, co-infection identification, diagnostic concordance, and treatment impact were analyzed. Metagenomic next-generation sequencing demonstrated high diagnostic sensitivity in the PICU setting, achieving a positive detection rate of 90.0% (80/89) and identifying a diverse spectrum of 103 pathogens, including 50.5% viruses, 43.7% bacteria, 38.8% co-infections (vs 11.6%), and 86.3% diagnostic concordance (vs 55.8%, P < 0.01). Among 46 patients included in the therapeutic outcome analysis (22 in the mNGS-guided group), 21 patients in the mNGS-guided group improved. Multivariate logistic regression analysis, adjusting for confounding factors (age, underlying diseases, PaO2/FiO2 ratio, PRISM III score, and preoperative antibiotic use duration), confirmed that mNGS-guided therapy was an independent protective factor for achieving the primary outcome (OR = 5.23, 95% CI: 1.87-14.61, P = 0.002) and secondary outcomes (C-reactive protein reduction ≥50%: OR = 4.89, 95% CI: 1.72-13.93, P = 0.003; oxygenation improvement: OR = 5.67, 95% CI: 1.98-16.21, P = 0.001). Metagenomic next-generation sequencing demonstrated high diagnostic sensitivity in the PICU setting, guiding precision therapy, and improving prognosis. IMPORTANCE: It supports metagenomic next-generation sequencing (mNGS) as a supplementary tool for pediatric intensive care unit (PICU) refractory infections, guides anti-infective adjustments, and informs tiered diagnostic pathways for resource-limited settings to optimize cost-effectiveness.

Humans↗

PubMed2024

Universal Identification of Pathogenic Viruses by Liquid Chromatography Coupled with Tandem Mass Spectrometry Proteotyping.

Accurate and rapid identification of viruses is crucial for an effective medical diagnosis when dealing with infections. Conventional methods, including DNA amplification techniques or lateral-flow assays, are constrained to a specific set of targets to search for. In this study, we introduce a novel tandem mass spectrometry proteotyping-based method that offers a universal approach for the identification of pathogenic viruses and other components, eliminating the need for a priori knowledge of the sample composition. Our protocol relies on a time and cost-efficient peptide sample preparation, followed by an analysis with liquid chromatography coupled to high-resolution tandem mass spectrometry. As a proof of concept, we first assessed our method on publicly available shotgun proteomics datasets obtained from virus preparations and fecal samples of infected individuals. Successful virus identification was achieved with 53 public datasets, spanning 23 distinct viral species. Furthermore, we illustrated the method's capability to discriminate closely related viruses within the same sample, using alphaviruses as an example. The clinical applicability of our method was demonstrated by the accurate detection of the vaccinia virus in spiked saliva, a matrix of paramount clinical significance due to its non-invasive and easily obtainable nature. This innovative approach represents a significant advancement in pathogen detection and paves the way for enhanced diagnostic capabilities.

Tandem Mass Spectrometry↗

PubMed2025

Identification of pathogenic variants in six Chinese families with keratoconus of autosomal dominant inheritance: pathogenicity analysis and variable phenotype.

PURPOSE: Keratoconus (KC) is a bilateral, asymmetric disease causing corneal thinning, irregular astigmatism, and vision decline, with unclear etiology. This study aims to investigate pathogenic variants of candidate genes in Chinese KC families via whole exome sequencing (WES). METHODS: The Pentacam 3D anterior segment analysis system was applied for keratectasia detection, and the Corvis ST was used for corneal biomechanics measurement. Probands from KC families were screened via WES and further verified in other family members through Sanger sequencing. Additionally, qPCR was used to validate copy number variants and identify pathogenic gene loci. The identified variants were then classified according to the Standards and Guidelines for the Interpretation of Sequence Variants published by the American College of Medical Genetics and Genomics (ACMG). Finally, STRING protein-protein interaction (PPI) networks analysis was performed to investigate interactions among candidate gene-related proteins. RESULTS: Using WES, four heterozygous missense variants were detected in the ZNF469, KRT12, COL8A2, and COL18A1 genes: c.4384G > A: p.Asp1462Asn, c.1229T > G:p.Val410Gly, c.505A > G:p.Ile169Val, and c.1159G > A:p.Gly387Arg. Additionally, a heterozygous frameshift variant was detected in the PMS2 gene: c.1551_1572del:p.Ser517Argfs*71. The affected parents carried the same variants as the probands verified by Sanger sequencing. A copy number variant was detected in the DPP6 gene: seq[GRCh38] dup(7)(q36.2q36.2) chr7:g.153782360_ 153982491dup. According to ACMG guidelines, ZNF469, KRT12, COL8A2, and COL18A1 gene variants are Likely Pathogenic; PMS2 and DPP6 gene variants are Pathogenic. STRING analysis highlights a tightly interconnected network centered on COL8A2, involving COL18A1, FN1, ZNF469, and KRT12. DPP6 was involved in KC via affecting FN1. In four of six autosomal dominant KC (adKC) families, affected parents had the same variants as probands but milder phenotypes. CONCLUSION: In this study, six novel variants in ZNF469, KRT12, COL8A2, COL18A1, PMS2, and DPP6 were linked to adKC. Family phenotypes showed variable expressivity with irregular dominance inheritance. Abnormal KC-related gene protein expression may contribute to corneal structural instability. This study broadened KC genetic screening candidates and suggested genetic testing could aid early KC diagnosis and intervention.

Adult↗

PubMed1978

Laboratory identification of pathogenic Neisseria with special regard to atypical strains: an evaluation of sugar degradation, immunofluorescence and co-agglutination tests.

Sugar degradation tests (SDT) were compared with immunofluorescence (IFL) and co-agglutination (COA) tests for the diagnosis of Neisseria gonorrhoeae (GC) and Neisseria meningitidis (MC). Somewhat more than 5% of the GC strains and 8% of the MC strains were misinterpreted by SDT. On most occasions the disagreement between SDT and serological tests was due to the inability of the MC strains (less so for GC strains) to degrade sugars correctly. Because of this, three out of 15 strains (20%) from pharyngeal specimens were primarily considered to be GC by SDT but were identified as MC by COA tests. Deficiencies in sugar degradations were also found in a group of clinical problem strains. Many of them were unable or had a decreased ability to degrade glucose or maltose but were diagnosed distinctly as MC by the COA test. There were no false positives with the IFL or COA tests, but 2% of the GC strains and 26% of the MC carrier strains (non-groupable) were not identified by COA. Both IFL and COA tests are good adjuncts to SDT for the diagnosis of GC and clinically significant MC, since the results are reliable and the tests rapid and simple to perform.

Agglutination Tests↗

PubMed1978

[Serological analysis of Actinomyces viscosus and identification of pathogenic human strains].

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Actinomyces↗

PubMed2026

Integrating metagenomic next-generation sequencing into a multimodal diagnostic framework for spinal infection: enhancing etiological identification and clinical prediction.

BACKGROUND: Spinal infection (SI) remains diagnostically challenging because of heterogeneous etiologies, nonspecific clinical manifestations, and the limited sensitivity of conventional microbiological approaches, particularly following empirical antimicrobial exposure. Although metagenomic next-generation sequencing (mNGS) enables unbiased pathogen detection, its incremental clinical value beyond pathogen identification and its role within integrated diagnostic strategies remain incompletely established. METHODS: We retrospectively analyzed 208 consecutive patients with suspected SI between August 2022 and August 2025. Final diagnoses were established using a multidisciplinary-adjudicated composite reference standard incorporating clinical, radiological, microbiological, and histopathological evidence. The diagnostic performance of mNGS was compared with conventional culture and histopathology. Furthermore, multimodal predictive models integrating clinical variables and microbiological information were developed using L1-regularized logistic regression. RESULTS: In the comparative cohort, mNGS achieved a significantly higher diagnostic yield than culture (66.5% vs. 27.41%, P < 0.001). Among confirmed SI cases, mNGS demonstrated higher sensitivity than conventional culture (91.67% vs. 40.15%, P < 0.001). mNGS identified a substantially broader pathogen spectrum, ranging from fastidious organisms such as Mycobacterium tuberculosis and Brucella to rare pathogens including Talaromyces marneffei and Coxiella burnetii, and maintained robust sensitivity (98.2%) despite prior antibiotic exposure. While an integrated clinical model achieved an AUC of 0.916, mNGS as a standalone modality provided superior discriminative power (AUC = 0.889) compared to histopathology (AUC = 0.836), the Conventional Biomarker Model (AUC = 0.742), and culture (AUC = 0.693). CONCLUSIONS: mNGS is a high-yield diagnostic tool for spinal infection, particularly in culture-negative and antibiotic-pretreated scenarios. Integrating mNGS into a multimodal clinical framework facilitates etiological clarity and precision antimicrobial therapy.

Humans↗

PubMed2026

Comparative performance of portable DNA extraction protocols and bioinformatics workflows for rapid detection of gram-negative bacteria and antimicrobial resistance using Oxford Nanopore sequencing.

Oxford Nanopore Technology (ONT) enables rapid, portable pathogen identification and antimicrobial resistance (AMR) detection, but the reliability of downstream genomic analyses is highly dependent on DNA extraction quality, particularly in resource-limited settings. This study comparatively evaluated four portable bacterial DNA extraction protocols derived from three commercial kits to determine their impact on nanopore sequencing performance, bioinformatics workflow completion, and field deployability. Six gram-negative bacterial isolates (Escherichia coli, n = 4; Pseudomonas sp., n = 1; and Salmonella sp., n = 1) were processed using four extraction protocols: SwiftX DNA, SwiftX DNA with proteinase K (ProtK), SwiftX ParaBact, and NucleoSpin Microbial. Twenty-four resulting DNA extracts were sequenced on a single multiplexed MinION R10.4.1 flow cell. Sequencing data were analyzed using validated Galaxy-based generic and species-specific pipelines. Workflow completion was defined as successful progression through quality control, assembly, virulence, plasmid, and AMR detection modules. DNA purity varied substantially by extraction protocol and was strongly associated with successful workflow completion (Kruskal-Wallis, P = 0.0006). Accordingly, NucleoSpin Microbial achieved 100% workflow completion, and SwiftX ParaBact achieved 83%, while both SwiftX DNA-based protocols failed to complete full workflows. Importantly, key AMR genes required to classify isolates as multidrug-resistant were consistently detected using both NucleoSpin Microbial and SwiftX ParaBact extractions. However, NucleoSpin Microbial assemblies showed significantly higher contiguity and enabled a broader, more complete detection of virulence factors, pathogenicity islands, plasmid replicons, and accessory AMR genes, reflecting enhanced genomic resolution.IMPORTANCERapid whole-genome sequencing is increasingly used to detect antimicrobial resistance and guide public health responses, but its reliability depends strongly on how bacterial DNA is extracted. In this study, we have shown that DNA extraction method choice has a major impact on Oxford Nanopore sequencing performance across clinically relevant gram-negative bacteria. While silica column-based extraction maximized genomic completeness and analytical depth, paramagnetic bead-based reverse purification offered superior portability with sufficient resolution for frontline AMR surveillance. These findings highlight a practical trade-off between field deployability and high-resolution genomic characterization in low-resource settings.

DNA extraction↗

PubMed2026

Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends.

Antimicrobial resistance (AMR) constrains effective treatment and carries implications for infection control, surveillance, and public health. The World Health Organization (WHO) priority bacterial pathogen framework has intensified the need for diagnostic innovation by redefining research priorities around organisms combining high disease burden with complex resistance profiles. Molecular diagnostics have accordingly moved beyond culture-based workflows, integrating rapid pathogen identification, resistance-marker detection, genomic surveillance, and clinical decision support. The present study conducted a bibliometric mapping of the literature on WHO priority pathogens. Rather than addressing resistance at a general level or a single pathogen or technology, it integrates priority pathogens, molecular platforms, and resistance markers within a single framework, tracing their joint thematic and temporal evolution along an explicit pathogen-platform-marker axis. Scopus-indexed articles and reviews (2000-2025) were retrieved, yielding 1746 publications after screening adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Analyses used Bibliometrix/Biblioshiny, R, and VOSviewer. The literature expanded markedly after 2018, led by China and the United States. Methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis, Enterococcus faecium, and the Enterobacterales-carbapenemase axis constituted the principal thematic cores, whereas conventional polymerase chain reaction (PCR)/nucleic acid amplification testing (NAAT) and whole-genome sequencing were the dominant platforms. Overall, the field has evolved from pathogen detection into an AMR-centered translational domain encompassing resistance prediction, genomic epidemiology, surveillance, and clinical decision support. Diagnostic development, stewardship, and surveillance depend on hybrid workflows coupling rapid marker-targeted assays with genome-based characterization, delivering actionable resistance within clinically meaningful timeframes, and extending coverage to underrepresented pathogens and platforms.

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

TaxTriage: an open-source metagenomic sequencing data analysis pipeline enabling putative pathogen detection.

MOTIVATION: TaxTriage is a comprehensive pathogen identification workflow designed for both short- and long-read untargeted DNA and RNA sequencing data. Combining read classification, mapping, and de novo assembly approaches, putative pathogens are identified through comparisons to curated pathogens and abundance expectations from healthy cohort data. Flexible installation options are enabled using Nextflow™ (NF), including cloud deployment via NF Tower (Seqera Platform) and local installation on a variety of systems, including standalone installations without external internet access. Final analysis summaries are compiled into an Organism Discovery Report, which lists likely pathogens and supporting data, including a custom confidence score. RESULTS: Evaluation of published in silico, clinical, and outbreak datasets identified performance comparable to alternative cloud-based processing pipelines for expected pathogen and co-infection detection with similar sensitivity and increased specificity. To support both public health and veterinary diagnostics communities, customization options have been incorporated to enable improved performance for host species of interest. AVAILABILITY AND IMPLEMENTATION: Source code for TaxTriage is freely available at https://github.com/jhuapl-bio/taxtriage. TaxTriage v2.1.1 has been archived on Zenodo at https://zenodo.org/records/17081354 to permit reproducible analysis as described in this manuscript.

Software↗

PubMed2026

Rapid diagnosis of common, undetected, and uncultivable bloodstream infections from positive blood cultures using Oxford Nanopore sequencing: a metagenomic pipeline analysis.

BACKGROUND: Metagenomic sequencing can potentially transform clinical microbiology by enabling rapid pathogen identification and antimicrobial resistance (AMR) prediction in critically ill patients with bloodstream infections. However, the clinical use of metagenomic sequencing has been constrained by its speed, accuracy, and technical feasibility. Our aim was to develop and evaluate a direct-from-positive blood culture workflow using Oxford Nanopore sequencing that overcomes these limitations and delivers rapid, accurate results. METHODS: In this metagenomic pipeline analysis, 211 positive (130 aerobic and 81 anaerobic) and 62 negative (30 aerobic and 32 anaerobic) randomly selected blood cultures were processed from Oxford University Hospitals for comparing species identification, AMR detection, and time-to-result against standard culture-based diagnostics performed by the hospital's routine microbiology laboratory. Species prediction was performed using Kraken2 with a comprehensive standard database, applying heuristic and random forest classification models. Additionally, we benchmarked AMR classification tools and databases, including ResFinder, CARD, and NCBI AMRFinderPlus. FINDINGS: Across all samples, our method achieved 97% sensitivity and 94% specificity for species identification compared with that of routine culture and matrix-assisted laser desorption ionisation time-of-flight-based diagnostics; both sensitivity and specificity increased to 100% after adjudication of plausible additional infections. We detected 19 additional infections (13 polymicrobial, five previously unidentifiable, and one in a culture-negative sample) and delivered species identification results within 3 h 20 min (IQR 3 h 7 min-3 h 27 min), approximately 10 h earlier than routine diagnostic methods. For the ten most common clinically relevant pathogens, our method yielded AMR results 20 h earlier than current antimicrobial susceptibility testing, with an overall sensitivity of 88% and specificity of 93%. Performance varied by species. For Staphylococcus aureus, the AMR prediction sensitivity was 100% and specificity was 99%, and for Escherichia coli, the prediction sensitivity was 91% and specificity was 94%. INTERPRETATION: These findings show that metagenomic sequencing has the potential to rapidly and comprehensively detect pathogens and AMR in bloodstream infections. Integration into clinical practice could help to close diagnostic gaps, reduce empirical antibiotic use, and enable rapid targeted treatment. Nonetheless, improvements in AMR prediction for some species and drugs, along with further multisite validation, are required before clinical implementation. FUNDING: National Institute for Health Research (NIHR) Oxford Biomedical Research Centre.

Humans↗

PubMed2026

Employing Metagenomics Capture targeted next-generation sequencing for the etiological diagnosis of bloodstream infections.

BACKGROUND: Bloodstream infections (BSIs) represent a significant public health concern. Metagenomic Capture targeted next-generation sequencing technology, as a newly emerging method for pathogen detection, has been applied in the etiological diagnosis of various infectious diseases and demonstrates good diagnostic efficacy. However, there is relatively limited research on the diagnostic value of this technology for the etiological diagnosis of BSIs. METHODS: A comprehensive retrospective analysis was performed on patients suspected of having BSIs who were admitted to the Affiliated Guangdong Second Provincial General Hospital of Jinan University in 2024. These patients underwent both blood culture analysis and Metagenomic Capture targeted next-generation sequencing technology for diagnostic testing, and a detailed comparison of the results was conducted. RESULTS: It was found that the Metagenomic Capture-targeted next-generation sequencing method has a shorter time to result [1.33 (1.18 - 1.69) vs 2.73 (1.89 - 3.84) days, p < 0.001], more pathogenic microbial species detected, higher positive detection rate and higher sensitivity than blood culture. CONCLUSIONS: Metagenomic Capture targeted next-generation sequencing technology is a promising tool for pathogen identification in BSIs, offering substantial methodological advantages in terms of turnaround time, detection breadth, and sensitivity. These diagnostic performance characteristics support its potential utility in clinical microbiology practice.

Humans↗

PubMed2026

Discovering common and population-specific QTLs for leaf rust resistance in different Barley populations.

Multi-population GWAS lead to identification of common and population-specific QTLs for leaf rust resistance in barley. Genome-wide association studies (GWAS) are a powerful tool for detecting genetic markers associated with traits of interest. However, these studies are typically restricted to a single population, and transferability of identified marker effects across populations is challenged by population differences in linkage, allele frequencies, epistatic effects, and environmental context. When comparing GWAS results between populations, a lack of overlapping signals is often interpreted as a lack of common quantitative trait loci (QTLs), although such discrepancies may result from differences in statistical power to detect signals. In barley (Hordeum vulgare L.), where genetic leaf rust resistance is rapidly overcome by evolving pathogens, identification of cross-population robust and potentially transferable resistance loci is a key task. Here, we present a mixed model approach for multi-population GWAS that estimates correlated marker effects in multiple populations and use this to test for significant effects across and within populations. Applying this model to four barley breeding populations revealed both common and population-specific QTL effects for leaf rust resistance, including loci colocalizing with known Rph genes and novel regions with plausible candidate genes. Multi-population GWAS increased power, revealing signals not detected by GWAS within populations. We categorized the reported QTLs into three groups based on marker-associated allele effects: (1) consistent effect direction across populations, (2) differing effect direction across populations, and (3) present in a single population. The study highlights the transferability and limitations of leaf rust resistance QTLs across different barley populations and provides a general statistical framework to support robust marker-assisted selection across populations.

Quantitative Trait Loci↗

PubMed2026

[Microbiological Characterization of Exacerbations in Severe Asthma and Their Impact on Therapeutic Decision-Making].

INTRODUCTION: Severe asthma (SA) exacerbations impose a substantial healthcare burden. Microbiological characterization using molecular techniques may improve pathogen identification and contribute to a more individualized therapeutic approach. OBJECTIVE: To characterize the microbiological profile of exacerbations in patients with severe asthma and to analyze the prescription patterns for antibiotics (ATB) and systemic corticosteroids (SC). METHODS: This retrospective observational study was conducted in a Severe Asthma Unit. A total of 103 exacerbations were investigated using conventional microbiological methods and multiplex polymerase chain reaction (FilmArray™) performed on respiratory samples. Bacterial findings were classified according to operational criteria compatible with infection or colonization based on genomic load and culture results. Associations between clinical, microbiological, and therapeutic variables were explored using univariate analyses. RESULTS: Microbiological detection was achieved in 78.6% of exacerbations. Viruses were identified in 59.2% of episodes, with rhinovirus representing the predominant pathogen (62.3% of viral detections). Bacteria were identified in 53.4% of exacerbations (H. influenzae 36,6%), frequently in association with viral coinfection. Bronchiectasis was associated with a higher probability of bacterial detection (OR 2.50; p = 0.031). ATB and SC were prescribed in 61.2% and 44.6% of exacerbations, respectively, with frequent use of combination therapy. No significant differences in overall microbiological detection rates were observed according to biologic therapy status. Considerable microbiological variability was observed across recurrent exacerbations in the same patient. CONCLUSIONS: Microbiological findings were common during severe asthma exacerbations, with respiratory viruses, particularly rhinovirus, being the most frequently identified pathogens. Bronchiectasis was associated with higher rates of bacterial detection and ATB use. The marked variability observed between episodes supports the potential value of individualized microbiological assessment during exacerbations and warrants prospective studies aimed at optimizing therapeutic decision-making.

Biologic therapies.↗

PubMed2026

Characterisation of Carbapenem-Resistant Raoultella planticola and Structural Analysis of NDM Composite Plasmids.

OBJECTIVE: This study aimed to investigate the molecular characteristics, resistant plasmid structures and phylogeny of a carbapenem-resistant Raoultella planticola (CRRP) strain from a patient with pneumonia to inform antimicrobial resistance control strategies. METHODS: We performed strain identification using MALDI-TOF MS, the BD Phoenix 100 system and whole-genome sequencing (WGS). We assessed antimicrobial susceptibility and resistance gene transfer using PCR, conjugation and stability assays, plasmid structure using a bioinformatics tool and phylogeny using a core-genome phylogenetic tree. RESULTS: WGS confirmed the isolate as R. planticola (average nucleotide identity (ANI) > 98.9% with reference type strains), co-harbouring blaKPC-2 and blaNDM-1. It was resistant to 19 antimicrobial agents and susceptible to only polymyxin, amikacin and chloramphenicol. Resistance genes were present on two conjugative plasmids: pzwx_KPC (IncFIA) and pzwx_NDM (a novel repFIB/repHI5B hybrid assembled via non-homologous end joining). Both plasmids demonstrated efficient transfer and stable inheritance over 12 passages. pzwx_KPC was highly homologous to plasmids from Klebsiella pneumoniae. Phylogenetic analysis revealed the closest relationship with German R. planticola strains. CONCLUSION: CRRP carries highly transmissible and stable resistance plasmids. Strengthened monitoring in immunocompromised patients and improved environmental disinfection are recommended. The risk of misidentification by automated systems underscores the importance of WGS for accurate pathogen identification.

Carbapenem resistance↗

PubMed2026

DNA sequencing for microbial surveillance in cystic fibrosis airways: advances, challenges, and clinical translation.

SUMMARYDNA sequencing has revolutionized microbial surveillance in cystic fibrosis (CF), transforming pathogen identification from culture-dependent to total microbial community identification using molecular-based approaches. Techniques such as 16S rRNA gene sequencing have uncovered the complexity of the CF airway microbiome, while shotgun metagenomics, metatranscriptomics, and viromics now provide strain-level, functional, and viral insights beyond bacterial identification. Despite these advances, key technical and logistical challenges remain, including the processing of high-viscosity sputum samples, overwhelming host DNA contamination, managing large data sets, and the integration of complex bioinformatic outputs into clinical workflows. Emerging innovations such as host DNA depletion protocols, targeted enrichment panels, and adaptive sampling on Oxford Nanopore platforms are helping to overcome these barriers, improving microbial recovery and sequencing efficiency. As cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies are changing the lives of people with cystic fibrosis (pwCF), sequencing offers an unprecedented opportunity to track potential microbial adaptation in response. This review investigates current advances, limitations, and translational opportunities in DNA sequencing for CF airway microbiome surveillance, highlighting how these technologies can help reshape research and clinical microbiology in the post-modulator era.

Cystic Fibrosis↗

PubMed2024

Effect of Metagenomic Next-Generation Sequencing on Clinical Outcomes of Patients With Severe Community-Acquired Pneumonia in the ICU: A Multicenter, Randomized Controlled Trial.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) was previously established as a method that can increase the pathogen identification rate in patients with severe community-acquired pneumonia (SCAP). RESEARCH QUESTION: What is the impact on clinical outcomes of mNGS of BAL fluid (BALF) in patients with SCAP in the ICU? STUDY DESIGN AND METHODS: A multicenter randomized controlled open-label clinical trial was conducted in 10 ICUs. Patients were randomized in a 1:1 ratio to undergo BALF assessment with conventional microbiological tests (CMTs) only (ie, the CMT group) or BALF assessment with both mNGS and CMTs (ie, the mNGS group). The primary outcome was the time to clinical improvement, defined as the time from randomization to either an improvement of two points on a six-category ordinal scale or discharge from the ICU, whichever occurred first. RESULTS: A total of 349 patients were randomized to treatment between January 1, 2021, and November 18, 2022; 170 were assigned to the CMT group and 179 to the mNGS group. In the intention-to-treat analysis, the time to clinical improvement was better in the mNGS group than in the CMT group (10 days vs 13 days; difference, -2.0 days; 95% CI, -3.0 to 0.0 days). Similar results were obtained in the per-protocol analysis. The proportion of patients with clinical improvement within 14 days was significantly higher in the mNGS group (62.0%) than in the CMT group (46.5%). There was no significant difference in other secondary outcomes. INTERPRETATION: We found that compared with the use of CMTs alone, mNGS combined with CMTs reduced the time to clinical improvement for patients with SCAP. CLINICAL TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR; www.chictr.org.cn/index.html; ChiCTR2000037894.

Humans↗