Water Research

Indo-Pacific humpback dolphin (Sousa chinensis) and finless porpoise (Neophocaena phocaenoides) are increasingly threatened across their native range, yet the relative influence of multiple stressors in shaping their population dynamics remains unclear. Current conservation strategies for both species are limited by incomplete data and limited assessment of affecting factors. Here, we integrated eDNA metabarcoding with Joint Species Distribution Modeling (JSDM) to assess how environmental gradients, pollution, and trophic associations interactively influence cetacean distributions in Hong Kong waters. We show that degraded water quality and intensified human activity negatively associated with cetacean occurrence, with clear species-specific responses to different stressors. S. chinensis covaried most strongly with Secchi disc depth, and presence of vessels, while N. phocaenoides was negatively associated with nitrate nitrogen and microbial pollution (sewage). The JSDM variance partitioning analysis highlighted that the occurrence of S. chinensis was primarily associated with anthropogenic disturbances (30.04%), followed by water physical properties (26.63%), whereas N. phocaenoides was more strongly associated with physical (40.9%) and anthropogenic disturbances (35.2%). By integrating eDNA and JSDM, our approach provides fine-scale diagnostics of species-specific vulnerabilities, supporting adaptive conservation strategies and guiding the realignment of protected areas to mitigate biodiversity loss in urbanized marine ecosystems. Environmental ImplicationOur study demonstrates that hazardous water pollutants, including microbial contamination, nutrient enrichment, and chemical stressors, vessel pressure, show strong, species-specific impacts on resident cetaceans in Hong Kong. By integrating eDNA metabarcoding with joint species distribution models, we provide a diagnostic framework that directly links pollutant profiles to ecological risk. These findings highlight that conventional conservation strategies overlooking pollution drivers are insufficient for marine megafauna persistence. Our approach offers an early-warning system for monitoring hazardous pollutants in coastal ecosystems and supports adaptive management strategies to mitigate biodiversity loss in urbanized seascapes.

Pacific salmon are keystone species to North Pacific freshwater, coastal, and oceanic ecosystems, but many populations have declined or become more variable in recent decades due to anthropogenic impacts and climate change. Long-term records are needed to understand past changes, identify ecosystem stressors, and guide restoration. We used sedimentary DNA (sedDNA), an emerging paleoecological approach offering broader taxonomic information than traditional methods, to reconstruct ecosystem changes across five Pacific salmon nursery lakes in British Columbia (Canada). DNA metabarcoding targeting the 18S ribosomal RNA gene V7 region was used to track shifts in eukaryotic communities including algae and invertebrates over centuries to millennia. Most lakes showed notable algal community shifts over the past two centuries, with declining green algae and rising diatom relative abundances. Chrysophytes and dinoflagellates also increased over the past century in most lakes, likely driven by stronger thermal stratification, which favored these motile and mixotrophic algae that are capable of vertical migration and flexible nutrient acquisition. We contextualized the trajectories of each core through an ordination analysis based on 98 lakes distributed across British Columbia, which identified land-use changes and longer growing seasons as potential drivers. Network analyses of the sedDNA time series revealed decreasing modularity and increasing connection across lakes, suggesting a shift in resilience mechanisms from between-module buffering by compartmentalized specialists to within-guild insurance via functional overlap among generalists. Our findings demonstrate that sedDNA provides taxonomically rich, long-term insights into aquatic ecological dynamics, which are foundational for understanding and protecting Pacific salmon nursery habitats.

Background Pseudomonas aeruginosa is a major cause of healthcare-associated infections in paediatric settings, where its persistence in moist environments such as hospital water and wastewater systems poses a particular risk to neonates and immunocompromised children. Aim The aim of this study was to showcase the long-term survival and transmission of P. aeruginosa in a large tertiary children's hospital in England which is crucial to develop strategies for water-safe care. Methods Environmental P. aeruginosa isolates were collected from taps, sinks, showers, and baths in augmented care areas of a 330-bed tertiary children's hospital built to NHS water-safety standards. Clinical isolates were classified as invasive (blood, cerebrospinal fluid, and bronchoalveolar lavage) or non-invasive (respiratory, urine, ear, abdominal, and rectal surveillance). Variable number tandem repeat (VNTR) profiles and metadata were extracted from PDF reports, de-identified, deduplicated, and curated using Python and R. Findings This retrospective study analysed nine-locus VNTR profiles of 457 P. aeruginosa isolates submitted to the UK Health Security Agency from a large tertiary children's hospital, identifying 56 isolate clusters (each with [≥]2 isolates), of which 19 (34%) contained at least one invasive isolate. The most persistent cluster (Cluster 1, n=20) spanned from July 2016 to September 2024, containing environmental and clinical (invasive and non-invasive) isolates. Conclusion These findings demonstrate long-term persistence of certain genotypes and temporal overlap between environmental and clinical isolates, highlighting the difficulty in detecting and eradicating P. aeruginosa in hospital water and wastewater systems and reinforcing the need for continuous rigorous water system controls.

Per- and polyfluoroalkyl substances (PFAS) are highly resistant to enzymatic C-F bond cleavage, and hydrolytic defluorination of long-chain PFAS has rarely been demonstrated. Here, we report selective hydrolytic defluorination of branched perfluorooctanoic acid (PFOA) isomers by a haloacid dehalogenase (4A) from Delftia acidovorans strain D4B. A fluoride-specific riboswitch biosensor was used for initial substrate screening, followed by scaled-up assays in which fluoride release was quantified using a fluoride ion-selective electrode. Defluorination products were subsequently identified by liquid chromatography-mass spectrometry (LC-MS). Although purified 4A (10 M) readily catalyzed hydrolytic defluorination of fluoroacetic acid, incubation of PFOA (0.5 mM) with purified 4A resulted in a statistically significant increase in fluoride release at elevated enzyme loading (500 M). High-resolution LC-MS/MS analysis revealed that defluorination products originated from minor branched PFOA isomers rather than linear PFOA. Molecular docking analyses supported catalytically plausible binding geometries for branched PFOA isomers, positioning the substrate -carbon within [~]4 [A] of the catalytic aspartate residue. These findings demonstrate previously unrecognized hydrolytic reactivity of a haloacid dehalogenase toward branched PFAS isomers and expand the known catalytic scope of the haloacid dehalogenase family. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/719434v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1c12fb1org.highwire.dtl.DTLVardef@224ae3org.highwire.dtl.DTLVardef@16293b7org.highwire.dtl.DTLVardef@d014b7_HPS_FORMAT_FIGEXP M_FIG C_FIG SYNOPSISEnzymatic defluorination of PFAS is rarely observed in environmental systems. This study identifies hydrolytic defluorination of branched PFOA isomers, improving understanding of PFAS defluorination at the enzyme level.

ObjectivesAntimicrobial resistance (AMR) in Gram-negative pathogens is driven by complex and coordinated molecular mechanisms that remain incompletely characterized. This study integrated phenotypic, genomic, and quantitative proteomic analyses to characterize multidrug-resistant (MDR) and extensively drug-resistant (XDR) Gram-negative bacteria circulating in an intensive care unit (ICU) in Northeastern Brazil. MethodsA total of 259 Gram-negative isolates collected between 2019 and 2021 underwent species identification, antimicrobial susceptibility testing, and targeted qPCR for resistance genes. Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa representing susceptible, MDR, and XDR phenotypes were selected for whole-genome sequencing and label-free quantitative proteomics. Differential protein abundance was assessed using Limma with |log2FC| > 1 and p < 0.05. ResultsK. pneumoniae (47%), A. baumannii (24%), and P. aeruginosa (21%) predominated. Carbapenem resistance reached 44%, 93%, and 61%, respectively, and MDR/XDR phenotypes occurred in >30% of isolates. Genomic analyses revealed dense resistomes with coexisting {beta}-lactamases (blaKPC, blaNDM, blaCTX-M, OXA) and widespread efflux systems. Proteomic profiling demonstrated phenotype-associated differences in outer membrane proteins, transport systems, regulatory proteins, and metabolic pathways. XDR isolates showed additional enrichment of envelope remodeling proteins, stress response mechanisms, and proteostasis-associated factors. ConclusionsMDR and XDR Gram-negative ICU pathogens exhibit coordinated resistance architecture characterized by accumulation of resistance genes and adaptive proteomic remodeling. Integrated multi-omics approaches provide mechanistic insight into antimicrobial resistance and support improved surveillance and therapeutic strategies. What is known?O_LIAntimicrobial resistance is a priority and a serious problem in global health, resulting in high rates of morbidity and mortality. C_LIO_LIKlebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa are on the World Health Organizations (WHO) priority list as major causes of morbidity and mortality worldwide. C_LIO_LIClassical characterization of susceptibility and resistance phenotypes does not capture the complexity of antimicrobial resistance and hampers effective control measures and actions to minimize the evolutionary dynamics of resistance in these bacteria. C_LI What is new?O_LIThe study characterizes the phenotypic pattern of antimicrobial susceptibility, the presence and sequencing of the resistome and virulome, and analyzes the label-free quantitative proteome of susceptible, MDR, and XDR phenotypes in strains of K. pneumoniae, A. baumannii, and P. aeruginosa circulating in hospital ICUs in Brazil. C_LIO_LIMDR and XDR gram-negative phenotypes are associated with a dense resistome, with widespread dissemination of beta-lactamase genes (bla_KPC, bla_NDM, bla_CTX-M, and OXA) and RND-type (MEXs) and acrAB-tolC efflux pumps, without changes in virulence genes. C_LIO_LIProteomic analysis demonstrated increased production of beta-lactamases, components of efflux pump systems, outer membrane protein synthesis, protection for oxidative stress mechanisms, proteins for iron acquisition, and systemic regulators. XDR strains additionally showed enhanced remodeling of the cell envelope, activation of proteostasis, and metabolic adaptation. C_LI

Endophytic fungi residing within medicinal plants are emerging as prolific sources of structurally diverse bioactive secondary metabolites with applications in drug discovery. Azadirachta indica (Neem) and Melia azedarach (Melia), members of the Meliaceae family, are renowned for their rich phytochemical composition; however, the contribution of their endophytic fungi communities to this chemical diversity remains largely unexplored. Herein, endophytic fungi were isolated from leaves and bark of Neem and Melia collected in Kenya and cultured under distinct physical conditions, solid (plates) and liquid (broth) media to assess how culture environment influences compound production. Compounds were extracted and analyzed using gas chromatography-mass spectrometry (GCMS) to profile the chemical diversity associated with each endophytic fungi, physical culturing state and host plant. GCMS analysis revealed that while the host plant identity influences the presence of specific compounds, the dominant determinant of chemical diversity was intrinsic biosynthetic capacity of the endophytic fungi themselves. Several compounds were unique to endophytic fungi cultures, highlighting their role as independent sources of bioactive compounds. Culture conditions moderately influence metabolite profiles, demonstrating the importance of optimizing growth environments in experimental design and natural product bioprospecting. From the Neem samples, we found 53 compounds uniquely present in the broth samples (consisting of Neem powder and endophytic fungi), 22 found exclusively with the endophytic fungi from the Neem, and 31 compounds shared between the broth and the endophytic fungi samples. In Melia samples, 109 compounds were uniquely present in broth samples from Melia plant (consisting of Melia powder and endophytic fungi), 22 compounds were found exclusively with the endophytic fungi from the Melia, and 55 were shared between the broth and the endophytic fungi samples. Our comparative analysis assessed the Neem and Melia endophytic fungi exclusive samples and reported 12 shared compounds. 10 compounds were unique to Neem and 10 unique to Melia; however, their identities varied between the two categories. While GCMS enabled the identification of volatile and semi-volatile metabolites, future studies employing complementary metabolomic approaches, such as liquid chromatography-mass spectrometry (LCMS), ultra-high-performance liquid chromatography MS/MS (UHPLC MS/MS), or nuclear magnetic resonance (NMR) spectroscopy, would expand coverage to non-volatile, polar, and high molecular weight compounds, providing a more comprehensive understanding of endophyte-derived chemical diversity. These findings provide insights into the interplay between medicinal plants and their endophytes and establish a foundation for leveraging endophytic fungi from Neem and Melia as scalable sources of structurally complex natural products for pharmaceutical and biotechnological applications while minimizing ecological impact.

Emerging infectious diseases and antimicrobial resistance (AMR) have surfaced as two major public health threats over the past two decades. Consequently, integrative surveillance systems capable of detecting both emerging pathogens and resistance-carrying bacteria are crucial. With advances in next-generation sequencing, simultaneous detection of pathogens and AMR is increasingly feasible. In this study, we used short-read metatranscriptomics complemented by total 16S rRNA metagenomic long-read sequencing to analyze paired oral and plasma samples from a cohort of febrile individuals at two locations in Senegal. Oral microbiomes differed in community composition between locations, and reduced diversity and richness were significantly associated with high fever. We identified at least one known pathogen in 15.33 % (23/150) of samples, with Borrelia crocidurae as the most frequently detected pathogen. We detected both pathogenic and non-pathogenic viruses in oral (10/72) and plasma (09/78) samples. Finally, we observed a high frequency of genes associated with resistance and virulence: 10% of samples expressed at least one AMR gene (ARG), and 24% expressed virulence factor genes. Resistance to widely used beta-lactam antibiotics was the most prevalent. Our findings provide critical data on oral and plasma microbiomes in the context of acute febrile illness in Senegal while expanding understanding of circulating ARGs.

Grapevine (Vitis vinifera L.) is highly prone to viral infections that pose a significant threat to global viticulture sustainability. Traditional detection methods, such as PCR and ELISA, are limited to well-known pathogens, highlighting the need for more comprehensive and unbiased approaches. Here, we present the development of a cost-effective viral enrichment system adapted to next-generation sequencing (NGS) for the detection and characterization of grapevine viruses. Our strategy leverages hybridization-based capture using biotin-labeled cDNA probes hereafter named "Chloro-Zero") designed to selectively deplete highly abundant host transcripts particularly plastid and ribosomal RNAs while preserving viral RNA. Probe design was informed by transcriptomic analysis of V. vinifera. We evaluated different subtractor-to-target RNA ratios, observing a consistent reduction of host RNA and a moderate enrichment of viral sequences. NGS analysis revealed improved recovery of low-abundance viral transcripts, with coverage levels comparable, to a certain extent, to those obtained using previously available commercial kits, but at a significantly lower cost. Although variability in depletion efficiency was observed, the results demonstrate the potential of this scalable and locally adaptable protocol for virome profiling in grapevines. By addressing key limitations of current depletion methods, our approach facilitates the detection of emerging viral threats and supports the development of more effective certification programs and sustainable management practices. Ongoing improvements in probe design and bioinformatic workflows are expected to enhance performance, providing a robust platform for broader applications in plant virology.

Predicting high-concentration viscosity of monoclonal antibodies such as IgG1 is crucial for their development as therapeutics for subcutaneous delivery. Unfortunately, traditional experimental rheometry methods for assessing viscosity are low-throughput. This study evaluates Self-Interaction Nanoparticle Spectroscopy (SINS) assays--specifically charge-stabilized SINS (CS-SINS) and PEG-stabilized SINS (PS-SINS)--for high-throughput viscosity prediction. We characterized 96 IgG1 antibodies, assessing SINS against in silico descriptors and dynamic light scattering (DLS) data. CS-SINS showed strong correlation with charge, offering limited additional utility. In contrast, PS-SINS provided orthogonal information; integrating it with in silico data and DLS significantly improved random forest model accuracy for binary viscosity classification. PS-SINS measurements in multiple buffers captured complementary information, achieving comparable accuracy without DLS. Importantly, PS-SINS scores exhibited a strong logarithmic relationship (r=0.98) with high-concentration viscosity in Fc variants of clinical antibodies, suggesting a direct mechanistic link. Furthermore, PS-SINS performed reliably with one column purified (protein A) samples, supporting its early-stage application. These findings establish PS-SINS as a high-throughput tool to accelerate the developability assessment of antibody candidates.

Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that mediate intercellular communication through the transfer of bioactive molecules. Their growing relevance in translational applications demands downstream purification workflows that are selective, scalable, and compatible with robust impurity control. Conventional EV isolation methods primarily rely on physicochemical properties such as size, density, or charge and therefore co-enrich overlapping EV fractions together with non-vesicular impurities. Here, we establish a Nanofitin(R)-based affinity chromatography workflow for selective enrichment of a CD81-positive EV fraction under EV-compatible elution conditions. Nanofitin(R) candidate NF06 was identified by ribosome display against the large extracellular loop of CD81 and combined nanomolar affinity with favorable release behavior while retaining binding after repeated regeneration cycles. Static screening with recombinant CD81 and HEK293-derived EVs identified 1 M arginine at pH 10 as the most suitable elution condition. Dynamic chromatography on a 1 mL column using tangential flow filtration-concentrated HEK293 conditioned medium achieved 66.9% overall recovery with an elution step yield of 57.7%. In parallel, dsDNA, host cell protein, and total protein were reduced by 2 to 3 log relative to conditioned medium. Nano flow cytometry showed enrichment of the CD81-positive EV fraction from 40% in conditioned medium to more than 90% in the eluates, together with a smaller and narrower particle size distribution. These results demonstrate that Nanofitin(R)-based affinity chromatography provides a practical route toward marker-defined EV enrichment that combines selective capture, EV-compatible release, and substantial impurity clearance in a chromatography-compatible process format.

Wastewater surveillance is increasingly used for antimicrobial resistance (AMR) monitoring in urban environments, but low-resource settings often lack a piped sewerage system. Instead, coprophagous flies--flies that ingest feces--may serve as composite samplers for monitoring fecal wastes present in terrestrial environments. We evaluated whether the class 1 integron-integrase gene intI1 was associated with genetic markers of AMR and fecal source tracking markers (FST) in coprophagous flies collected from latrine entrances and food preparation areas in low-income urban Maputo, Mozambique. We quantified intI1, an enteric 16S rRNA target (for normalization), three FST markers, and 30 ARG targets using qPCR. We normalized concentrations of intI1 and each target to enteric 16S rRNA. We fit linear mixed models with a random intercept for housing compound to estimate within-fly associations between log10 relative abundance of intI1 and log10 relative abundance of each target with and without adjustment for fly taxonomic group, capture location, and standardized fly mass. We also modeled per-fly unique ARG count (i.e., number of ARG targets detected) using Poisson regression. Of 188 flies assayed, 176 passed internal controls; intI1 and enteric 16S rRNA were detected in 95% and 96% of flies, respectively. Higher relative abundance of intI1 was positively associated with ARG and FST targets, with the strongest associations observed for sulfonamide-(sul1: {beta} = 0.87; 95% CI: 0.81, 0.94; sul2: {beta} = 0.81; 95% CI: 0.73, 0.89), tetracycline- (tetA: {beta} = 0.78; 95% CI: 0.70, 0.85; tetB: {beta} = 0.69; 95% CI: 0.60, 0.79), and trimethoprim-related (dfrA17: {beta} = 0.78; 95% CI: 0.70, 0.86) genes. Associations with FST markers were weaker (i.e., human mtDNA: {beta} = 0.46; 95% CI: 0.37, 0.55; human-associated Bacteroides: {beta} = 0.34; 95% CI: 0.25, 0.43). Higher relative abundance of intI1 was also associated with a greater number of ARGs detected: each 10-fold increase in intI1 was associated with an 8% higher expected unique ARG count (aRR=1.08, 95% CI: 1.04-1.12). These findings support the need for further research across different settings exploring intI1 carried by coprophagous flies as a potential standardized screening target for AMR surveillance in unsewered terrestrial environments.

Gut microbial biotransformation of poorly absorbable polyphenols into bioactive, bioavailable metabolites is increasingly recognized as a key mechanism underlying their health benefits of polyphenols. Microbial ellagic acid (EA)-to-urolithin conversion represents a typical example, but the environmental factors that facilitate such metabolism remain underexplored. We discovered that urolithin production by a gut commensal bacterium, Gordonibacter urolithinfaciens (G. uro), is metabolically repressed by arginine. To overcome such limitations, we developed PhenolBoost Medium (PBM) that induces a metabolic shift by suppressing the arginine deiminase pathway while activating pyruvate metabolism and hydrogen production in G. uro, thereby driving urolithin dehydroxylation. Transcriptomic profiling and 13C-isotopic tracing analysis revealed that pyruvate metabolism in PBM upregulates hydrogenase expression, facilitating the dehydroxylation of EA. PBM also promoted the complete conversion of EA to urolithin A in G. uro-Enterocloster bolteae co-culture, and other polyphenol biotransformations. In addition, co-culturing G. uro with hydrogen-producing Bacteroides species significantly increased urolithin production. Furthermore, an arginine-limited, pyruvate-enriched dietary regimen proved effective in vivo, resulting in significantly higher urolithin production and bioavailability in a mouse model. Our findings reveal the critical role of hydrogen in facilitating polyphenol dehydroxylation, and offer a viable nutritional strategy for boosting microbial production of beneficial metabolites from polyphenols.

Extreme environments impose severe physicochemical stresses that drive microorganisms to evolve specialized survival strategies. Microbial secondary metabolites determined by biosynthetic gene clusters (BGCs) are recognized as important mediators of microbial adaptation to environmental stress. However, their ecological roles, particularly habitat-dependent preferences across different environments, remain poorly understood. Although extreme environments provide opportunities to mine microbiomes for unique adaptations, such research is hampered by a lack of systematic overview of its genomic diversity, BGC diversity, and the relationships between them. Here, we constructed a standardized extremophilic genomic catalogue (SEGC) from 1,462 metagenomic samples spanning seven representative extreme habitats. The catalogue comprised 54,661 metagenome-assembled genomes representing 21,805 species, 66.1% of which were previously uncharacterized. With this catalogue, we identified 162,855 BGCs distributed across 81.5% of MAGs. Gene cluster family analysis showed the strong habitat dependence largely explained by species-level habitat specificity. Terpene biosynthetic pathways illustrated habitat-linked adaptive strategies, with hopan-22-ol biosynthesis enriched in acid mine, deep sea and hydrothermal plume environments, while retinal-based phototrophy predominated in cryosphere and saline-alkaline habitats. Metatranscriptomic analyses supported in situ activity of these pathways. In conclusion, we presented a global atlas of biosynthetic potential across extreme-environment microbiota and revealed habitat-dependent patterns of secondary metabolism linked to microbial survival.

Aim: To comprehensively characterize the salivary proteome in periodontitis using Orbitrap Astral data-independent acquisition mass spectrometry (DIA-MS), identify an atlas of differentially expressed proteins (DEPs), and develop a machine learning-derived multi-protein biomarker panel for non-invasive diagnosis of stage III/IV periodontitis. Materials and Methods: Unstimulated saliva samples from 199 participants (periodontal health/gingivitis, n=120; stage III/IV periodontitis, n=79) were analyzed by Orbitrap Astral DIA-MS. DEPs were identified, and pathway enrichment analysis was performed. A two-tier machine learning pipeline, integrating pathway-based feature selection with cross-validated evaluation, was applied to identify the optimal diagnostic panel. Results: Orbitrap Astral DIA-MS quantified 5,597 salivary proteins and 1,966 DEPs (|log2FC|>0.5, FDR<0.05). Pathway analysis identified 14 periodontitis-relevant KEGG pathways, including Th17 cell differentiation, IL-17 signaling, neutrophil extracellular trap formation, and complement and coagulation cascades. A four-protein panel (TEC, RAC1, MAPK14, KRT17) achieved an area under the curve (AUC) of 0.985 plus-or-minus sign 0.010, with 83% sensitivity and 100% specificity. The panel was corroborated using public datasets. Conclusions: To our knowledge, this study represents the first application of Orbitrap Astral DIA mass spectrometry in periodontitis research, establishing a disease-specific DEPs atlas and a salivary biomarker panel with high diagnostic accuracy for stage III/IV periodontitis, providing a foundation for future external validation studies.

Sequencing the respiratory tract transcriptome has the potential to provide insights into infectious pathogens and the hosts immune response. While DNA-based sequencing is more standard in clinical laboratories due to its stability, RNA assays offer unique advantages. RNA reflects dynamic physiological changes, and for RNA viruses, viral RNA particles directly represent copies of the viral genome, enabling greater diagnostic sensitivity. However, RNAs susceptibility to degradation remains a significant challenge, particularly in RNase-rich specimens like saliva. To address this, we conducted a systematic, combinatorial evaluation of 24 distinct mNGS workflows, crossing eight nucleic acid extraction methods with three RNA-Seq library preparation protocols. Remnant saliva samples (n = 6) were pooled and spiked with MS2 phage as a control. The SARS-CoV-2 virus was spiked into half of the samples, which were extracted using the eight different extraction methods (n = 3) and compared using RNA Integrity Number equivalent (RINe) scores and RNA concentration. The extracted RNA was then processed across the three library construction methods and subjected to short-read sequencing to assess all 24 combinations head-to-head. We compared methods based on viral read recovery and found that RINe and concentration did not correlate with viral detection. The Zymo Quick-RNA Magbead kit and the Tecan Revelo RNA-Seq High-Sensitivity RNA library kit were the extraction and library-preparation kits that yielded the most SARS-CoV-2 reads, respectively. Importantly, our combinatorial analysis revealed that any small variability attributable to different nucleic acid extraction methods was heavily overshadowed by differences in quality attributable to the RNA-Seq library preparation methods. These findings challenge the reliance on conventional RNA quality metrics for clinical metagenomics and underscore the need to redefine extraction quality standards for mNGS applications. IMPORTANCEmNGS is a powerful and unbiased approach towards pathogen detection that has mostly been applied to blood and cerebrospinal fluid samples. However mNGS has recently been applied to more areas including the respiratory pathogen detection space, with potential applications in both in-patient diagnostics and public health surveillance. Saliva samples are an ideal sample type for these use cases since they can be collected non-invasively. However, saliva is also a challenging sample type due to its high RNase activity and often yields low-quality nucleic acid. This study explores the feasibility of using saliva specimens in mNGS with contrived SARS-CoV-2 samples to optimize the combination of two factors: nucleic acid extraction and RNA-seq library preparation. Exploration in this area could enhance the sensitivity of saliva-based mNGS assays, with the goal of future expansion of this specimen type in clinical diagnostics and public health surveillance. Key PointsO_LIThe choice of RNA-Seq library preparation kit has a greater impact on pathogen detection than the nucleic acid extraction method. C_LIO_LIThe combination of Zymo Quick-RNA Magbead extraction kit and TECAN Revelo RNA-Seq High Sensitivity RNA library kit recovered the highest percentage of total SARS-CoV-2 reads. C_LIO_LIRNA quantity and RINe score do not correlate with viral read capture, indicating a need for an alternative metric to assess RNA quality for downstream mNGS clinical diagnostics. C_LI

Nowadays N95 facial mask has gain huge attention due to COVID19 pandemic situation and it serves as the prime PPE. Though the microbes can be restricted to get inside the human body due to the presence of mask temporarily, but over the time, bacteria and other microbes may get entrapped into the threads of the mask itself and thus acting as a storage chamber of microbes. It is necessary to eliminate them from the mask surface. To do so different floral structured Nano-ZnO with variable oriented arrangement of petals were fabricated on the surface of the N95 mask and further characterized through instrumentations including XRD, FTIR,UV-Vis, Fluorescence-Spectroscopy, SEM, DLS. The average crystallite size calculated for synthesized four different ZnO nanoflower were 25.19 nm, 23.46 nm, 27.27 nm and 31.78 nm (for glycerol, PEG, EDTA, Chitosan assisted) respectively. The antimicrobial activity was investigated by standard microbial broth dilution assay and Kirby-Bauer test which assured the inhibition of the bacterial growth. The MIC-MBC value of ZnO nanoflowers for E.coli and B. subtilis were found to be effective at dilution of 250 {micro}g/ml and 100 {micro}g/ml. Additionally a modified Kirby-Bauer assay has been designed to investigate the killing efficiency of the bacteria (E.coli). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/719592v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@48e5ecorg.highwire.dtl.DTLVardef@1ef03c5org.highwire.dtl.DTLVardef@e089ddorg.highwire.dtl.DTLVardef@17b2850_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. - Graphical AbstractC_FLOATNO C_FIG

Lipid metabolism is increasingly recognized as a hallmark of cancer, yet translating lipidomic discoveries into clinically actionable biomarkers remains constrained by analytical variability and limited standardized validation frameworks. This challenge is further compounded by a chicken-or-egg problem, where expensive standards and labelled internal standards are required to identify and quantitate target lipids, but the diagnostic importance of these targets is uncertain until they can be reliably measured. Previous work had indicated the potential of 48 lipid biomarker species for the prediction of breast cancer from plasma samples using high resolution liquid chromatography mass spectrometry. This study aimed to identify each of these 48 species and develop a quantitative method to determine the absolute concentrations of these lipids in plasma to provide the basis for the development of a clinical assay for use in breast cancer detection. In doing so, we present a pragmatic workflow that bridges lipid discovery with lipid identification and robust quantitative analysis. A curated library of 48 lipid species was established using authentic standards to verify plasma lipids through retention-time matching and high-resolution spectral comparison. In plasma, 41 lipids were confidently identified based on co-elution with standards and diagnostic fragment ions. Method qualification, including assessment of accuracy, precision, recovery, and linearity, was performed across all 48 lipids in parallel with identification, and 46 lipids ultimately met all predefined qualification criteria. Notably, practical constraints, including time, cost, and availability of authentic standards, necessitated performing identification and targeted method development in parallel, highlighting challenges inherent to translating lipidomics into commercial or clinical assays. This workflow provides a reproducible framework for harmonizing lipid identification and quantification, enabling the reliable integration of lipidomic data into biomarker discovery and clinical applications.

Microbial degradation of suspended and sinking organic carbon regulates long-term oceanic carbon storage by controlling the efficiency of the biological pump. Yet microbial controls on carbon export and remineralization remain poorly constrained, limiting predictions of how ocean carbon cycling will respond to climate change. Here, we combined in situ sampling with ship-based incubations to quantify prokaryote-driven removal rates of suspended and sinking total organic carbon (TOC). Samples were collected below the mixed layer during three stages of a spring Phaeocystis pouchetii bloom in the Labrador Sea. Phaeocystis blooms can dominate regional phytoplankton biomass and are expected to increase under future climate. Removal rates were used as a proxy for carbon lability and combined with 16S rRNA metabarcoding and carbon composition analyses to link microbial community structure with substrate characteristics. Removal rates of sinking particles (0.02-0.06 d-1) were an order of magnitude higher than those of suspended TOC (0.002 d-1) during bloom-decline and non-bloom. In contrast, during late-bloom, suspended carbon exhibited rates of 0.01 d-1, comparable to sinking particles, and was enriched in exopolymer-rich colonies. Prokaryotic community composition varied primarily among bloom stages rather than carbon fractions, indicating that bloom stage-- and thus particle origin and composition--was the dominant control on bacterial degradation and assembly. Bacterial diversity peaked where carbon was refractory and originated from mixed phytoplankton. Together, these results demonstrate that suspended Phaeocystis-derived carbon can be rapidly remineralized when blooms produce exopolymer-rich colonies and highlight bloom stage as key regulator of microbial carbon processing and biological pump efficiency.

Phenazines are redox-active microbial metabolites produced and secreted in diverse ecological contexts from soils to chronic infections. In these disparate environments phenazines can function variously as antibiotics, extracellular electron shuttles, and nutrient scavengers. Key to understanding the impact of these functions is a robust expectation of phenazine retention or diffusion in a given context. But predicting phenazine fate and transport is difficult because of the chemical complexity of their local microenvironments. To address this challenge, we measured the octanol water distribution coefficient (LogD) as a proxy for lipophilicity of three naturally occurring phenazines produced by the opportunistic pathogen Pseudomonas aeruginosa: phenazine-1-carboxylic acid, phenazine-1-carboxamide, and pyocyanin. We investigated the behavior of both oxidized and reduced forms of these phenazines across broad ionic strength and pH conditions. While the ionic context exerts only small effects, the pH and redox state contribute strongly and independently to changes in phenazine lipophilicity. The pH trends are expected, but the observed redox dependence is generally missed by existing lipophilicity calculation methods. Additional LogD measurements with 1-hydroxyphenazine and unsubstituted phenazine, together with density functional theory modeling of phenazines in their reduced and oxidized forms, reveal that intramolecular hydrogen bonding contributes significantly to the increased lipophilicity of reduced phenazines that possess H-bond accepting substituents in the 1-position. These results explain phenazine behavior in a biological context: redox state alone significantly alters retention of pyocyanin in planktonic P. aeruginosa cells, with the reduced species being predominantly retained by membranes. We propose that the modulation of phenazine lipophilicity in response to the local redox environment has evolved to give a competitive advantage to bacteria by retaining or dispersing these bioactive molecules. Beyond improving our understanding of natural phenazine fate in diverse microbial contexts, our results emphasize an oft-overlooked theme relevant to rational drug and electrochemical shuttle design: redox state matters.

Extracellular vesicles (EVs) are cell-secreted biological nanoparticles that play a crucial role in intercellular communication and are gaining increasing attention as diagnostic biomarkers, therapeutic agents, and drug delivery vehicles. Consequently, the development of robust and sensitive methods for their characterization is essential. Herein we present the use of a microscope-mounted nanofluidic device for direct size determination and multi-parametric (3-color) fluorescence-based phenotyping of single biological nanoparticles that are in the size range of 20-200 nm in a method we denote Nano-SMF (SMF; size and multiplexed fluorescence). We demonstrate that it is possible to accurately determine the size of nanoparticles by analyzing their one-dimensional Brownian motion during directional flow through nanochannels, achieving size distributions for monodisperse nanoparticle solutions that are on par with TEM analysis, and size discrimination of nanoparticle mixtures that is significantly improved compared to conventional nanoparticle tracking analysis (NTA). Furter, we demonstrate that the method can be applied to analyze EVs directly in minute volumes of cell supernatant, avoiding pre-isolation or concentration steps. The method was applied to phenotype CD63- and CD81-positive EVs from a human embryonic kidney cell model, demonstrating that vesicle sub-populations defined by these two tetraspanin biomarkers differ significantly in size.