Culture-based Methods Research Articles

Affinity of Malassezia and Other Yeasts for Pulmonary Lipids

Pulmonary surfactant, the primary substance lining the epithelium of the human Lower Respiratory Tract (LRT), is rich in lipids, with dipalmitoyl-phosphatidylcholine (DPPC) being the most abundant. Although surfactants are known to have antifungal activity against some yeast species, the significant presence of species like Malassezia restricta in the lung mycobiome suggests that these yeasts may exhibit some level of lipo-tolerance or even lipo-affinity for pulmonary lipids. This study explored the affinity and tolerance of yeasts, identified as significant members of the lung microbiome, to pulmonary lipids through culture-based methods. Eleven species from the genera Malassezia, Candida (including the new genera Nakaseomyces and Meyerozyma), and Cryptococcus were tested for their growth on media containing pulmonary lipids such as DPPC and commercial porcine surfactant and in other culture medium that contain non-pulmonary lipids such as glycerol monostearate and tweens. The yeasts’ lipo-affinity or lipo-tolerance was assessed based on their growth on these lipids compared to standard media, specifically Modified Leeming Notman Agar (MLNA) for Malassezia species and Sabouraud Dextrose Agar (SDA) for the other genera. The addition of DPPC or surfactant to the media enhanced the growth of most Malassezia yeasts and some Cryptococcus species. C. parapsilosis, Meyerozyma guilliermondii and Cryptococcus neoformans s.s. showed similar growth to that on the standard media, while the other yeasts primarily demonstrated lipo-tolerance without lipo-affinity for these compounds. To our knowledge, this is the first report on the influence of pulmonary lipids on the in vitro growth of Malassezia spp. and other yeast members of the lung mycobiome. Some yeasts, such as Malassezia restricta, commonly found in the lower respiratory tract (LRT), exhibit specific affinity for lung lipids like DPPC and commercial porcine surfactant. This finding suggests that lung lipids may play a significant role in shaping the LRT mycobiome.

Advancements in Detection Methods for Salmonella in Food: A Comprehensive Review

Non-typhoidal Salmonella species are one of the leading causes of gastrointestinal disease in North America, leading to a significant burden on the healthcare system resulting in a huge economic impact. Consequently, early detection of Salmonella species in the food supply, in accordance with food safety regulations, is crucial for protecting public health, preventing outbreaks, and avoiding serious economic losses. A variety of techniques have been employed to detect the presence of this pathogen in the food supply, including culture-based, immunological, and molecular methods. The present review summarizes these methods and highlights recent updates on promising emerging technologies, including aptasensors, Surface Plasmon Resonance (SPR), and Surface Enhanced Raman Spectroscopy (SERS).

What Is the Accuracy of 16S PCR Followed by Sanger Sequencing or Next-generation Sequencing in Native Vertebral Osteomyelitis? A Systematic Review and Meta-analysis.

Identifying a microorganism in patients with native vertebral osteomyelitis presents diagnostic challenges. Microorganism identification through culture-based methods is constrained by prolonged processing times and sensitivity limitations. Despite the availability of molecular diagnostic techniques for identifying microorganisms in native vertebral osteomyelitis, there is considerable variability in reported sensitivity and specificity across studies, leading to uncertainty in their clinical utility. What are the sensitivity, specificity, and diagnostic odds ratios for 16S broad-range PCR followed by Sanger sequencing (16S) and metagenomic next-generation sequencing (NGS) for detecting bacteria in native vertebral osteomyelitis? On June 29, 2023, we searched Cochrane, Embase, Medline, and Scopus for results from January 1970 to June 2023. Included studies involved adult patients with suspected native vertebral osteomyelitis undergoing molecular diagnostics-16S bacterial broad-range PCR followed by Sanger sequencing and shotgun or targeted metagenomic NGS-for bacteria detection. Studies involving nonnative vertebral osteomyelitis and cases of brucellar, tubercular, or fungal etiology were excluded. The reference standard for the diagnosis of native vertebral osteomyelitis was a composite clinical- and investigator-defined native vertebral osteomyelitis diagnosis. Diagnostic performance was assessed using a bivariate random-effects model. Risk of bias and diagnostic applicability were evaluated using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. After a manual screening of 3403 studies, 10 studies (5 on 16S, 5 on NGS) were included in the present analysis, from which 391 patients were included from a total of 958 patients overall. Quality assessment via QUADAS-2 criteria showed moderate risk of bias and good applicability. 16S showed 78% (95% confidence interval [CI] 95% CI 31% to 96%) sensitivity and 94% (95% CI 73% to 99%) specificity, whereas NGS demonstrated 82% (95% CI 63% to 93%) sensitivity and 71% (95% CI 37% to 91%) specificity. In addition, the diagnostic ORs were 59 (95% CI 9 to 388) and 11 (95% CI 4 to 35) for 16S and NGS, respectively. Summary receiver operating characteristic curves showed high test performance for 16S (area under the curve for 16S 95% [95% CI 93% to 97%] and for NGS 89% [95% CI 86% to 92%]). Certainty in estimates was moderate because of sample size limitations. This meta-analysis found moderate-to-high diagnostic performance of molecular methods on direct patient specimens for the diagnosis of native vertebral osteomyelitis. When used as a complementary test to microbiological analyses, a positive 16S result rules in the diagnosis of native vertebral osteomyelitis, while further studies are needed to understand the role of NGS in the diagnosis of native vertebral osteomyelitis. When available, these tests should be used in addition to conventional microbiology, especially in complex cases with extensively negative standard microbiological test results, to detect fastidious bacteria or to confirm the causative bacteria when their isolation and pathogenicity are unclear. A large sample size is needed in future research to understand the use of these techniques as standalone tests for diagnosis. Level III, diagnostic study.

Antibiotic resistance of bioaerosols in particulate matter from indoor environments of the hospitals in Dhaka Bangladesh

The emergence and spread of antibiotic resistance in microorganisms pose significant challenges to public health, especially in hospitals. This study investigated the existence or occurrence of bacterial bioaerosol and their antibiotic resistance patterns in particulate matter (PM) collected from hospitals in the greater Dhaka region, Bangladesh. The real-time particulate matter concentrations (PM1.0, PM2.5, and PM10) were measured in four hospitals and two ambient locations. Air sampling was conducted using a filter-based method with a low-volume air sampler, while AEROCET-531 S (USA) was employed to monitor particulate matter concentrations. Bacterial bioaerosol concentration was determined using a culture-based method, and eleven bacterial species, including nine individual species, i.e., Staphylococcus aureus, Pseudomonas aeruginosa, P. stutzeri, Bacillus cereus, Acinetobacter schindleri, Proteus vulgaris, B. subtilis, Escherichia coli, and B. aerius, were isolated. Antibiotic susceptibility testing was conducted using the Kirby-Bauer disk diffusion method with 21 antibiotics. Bacterial isolates were detected using partial sequencing of the 16 S rRNA gene. Bioaerosol concentration ranged from 194.65 ± 22.48 CFU/m3 to 948.39 ± 84.14 CFU/m3, showing significant correlations with PM1.0 and PM2.5 concentrations (R2 = 0.80 and 0.85, respectively). All bacterial isolates collected from the hospitals exhibited resistance against four or more antibiotics, indicating multidrug resistance (MDR). Notably, the bacterial isolates displayed the highest resistance rate against ampicillin (90.90%), azithromycin (81.81%), erythromycin (81.81%), cefixime (81.81%), and cotrimoxazole (54.54%), among the tested antibiotics. Except B. aerius, all other bacterial isolates were associated with hospital-acquired infections (HAIs). These findings highlight the high rates of antibiotic resistance, underscoring the pressing requirement for infection control measures and continuous surveillance strategies in hospital settings. These findings emphasize the necessity for global hospital infection control strategies focusing airborne multidrug-resistant microorganisms.

Industrial hemp (Cannabis sativa L.) adapts to cadmium stress by reshaping rhizosphere fungal community

Increasing evidence indicates that plants under environmental stress can actively seek the help of microbes (‘cry-for-help’ hypothesis). However, empirical evidence underlying this strategy is limited under metal-stress conditions. Here, we employed integrated microbial community profiling in cadmium (Cd) polluted soil and culture-based methods to investigate the three-way interactions between the industrial hemp (Cannabis Sativa L.), rhizospheric microbes, and Cd stress. Results from the pot and three cycles of the successful hemp planting experiments showed that Cd stress significantly affected the composition of rhizosphere fungi in industrial hemp and induced enrichment of the fungal operational taxonomic unit (OTU)3 (Cladosporium). A representative of OTU3 (strain DM-2) was successfully isolated. In a hydroponic experiment, inoculation of DM-2 significantly increased the shoot length (by 25.84 %) and fresh weight (by 92.66 %) of hemp seedlings when compared to the absence of DM-2 under the Cd stress. The findings indicate that DM-2 inoculation could effectively alleviate the Cd stress in hemp seedlings. Metabolomic analysis of spent media with or without DM-2 revealed the association of DM-2 with the transformation of root exudates to melatonin, which may be a key chemical in plant-microbe interactions against abiotic stresses. The findings will inform efforts to manipulate the root microbiome to enhance plant growth in polluted environments.

Phage-induced disturbance of a marine sponge microbiome

BackgroundBacteriophages are known modulators of community composition and activity in environmental and host-associated microbiomes. However, the impact single phages have on bacterial community dynamics under viral predation, the extent and duration of their effect, are not completely understood. In this study, we combine morphological and genomic characterization of a novel marine phage, isolated from the Baltic sponge Halichondria panicea, and report on first attempts of controlled phage-manipulation of natural sponge-associated microbiomes.ResultsWe used culture-based and culture-independent (16S rRNA gene amplicon sequencing) methods to investigate bacterial community composition and dynamics in sponge microbiomes with and without the addition of phages. Upon application of a novel Maribacter specialist phage Panino under controlled conditions, we were able to detect community-wide shifts in the microbiome composition and load after 72 h. While bacterial community composition became more dissimilar over time in the presence of phages, species evenness and richness were maintained. Upon phage exposure, we observed the loss of several low-abundance constituent taxa of the resident microbiota, while other originally underrepresented taxa increased. Virulent phages likely induce community-wide disturbances, evident in changes in the total sponge microbial profile by specific elimination of constituent taxa, which leads to an increase in bacterial abundance of opportunistic taxa, such as the genera Vibrio, Pseudoalteromonas, and Photobacterium.ConclusionsOur findings suggest that sponge microbiome diversity and, by extension, its resilience depend on the maintenance of resident bacterial community members, irrespective of their abundance. Phage-induced disturbances can significantly alter community structure by promoting the growth of opportunistic bacteria like Vibrio and shifting the microbiome to a dysbiotic state. These insights highlight the role of bacteriophages in shaping microbiome dynamics and underscore the potential for phage application in managing bacterial community composition in marine host-associated environments.

Comprehensive evaluation of UV inactivation of E. coli using multiple gene targets and real-time quantitative PCR

UV disinfection is extensively used for wastewater disinfection and disinfection efficiency is commonly monitored using culture-based enumeration of E. coli. While culture-independent real-time quantitative polymerase chain reaction (qPCR) based methods are attractive due to faster turnaround and easier application, previous attempts with qPCR to monitor disinfection have been unsuccessful. In this study, the effect of UV irradiation on a pure E. coli culture was examined in collimated beam (CB) experiments and monitored using both a culturing technique and DNA damage quantified using both short amplicon (SA; <∼200 bp) qPCR and longer amplicon (LA; ∼500-bp) qPCR. The results, covering a UV dose range of 0 - 20 mJ/cm2 commonly used for wastewater disinfection, indicate a correlation between DNA gene damage quantified by both SA- and LA-qPCR and the decline in E. coli observed through culture-based methods. This demonstrates the potential of qPCR to serve as rapid alternative for monitoring wastewater disinfection efficacy. Furthermore, LA-qPCR was observed to be more sensitive than SA-qPCR. The results using LA-qPCR also revealed that UV exposure caused widespread and indiscriminate damage to E. coli’s genome, which is considered critical for its function and survival. The combined effect of UV on E. coli’s ability to function, grow or repair damage is suggested as the reason for the decline in culturability observed.

Oral dysbiosis initiates periodontal disease in experimental kidney disease.

It is presently unclear why there is a high prevalence of periodontal disease in individuals living with chronic kidney disease (CKD). Whilst some have argued that periodontal disease causes CKD, we hypothesised that alterations in saliva and the oral microenvironment in organisms with kidney disease may initiate periodontal disease by causing dysbiosis of the oral microbiota. Experimental kidney disease was created using adenine feeding and subtotal nephrectomy in rats, and by adenine feeding in mice. Loss of periodontal bone height was assessed using a dissecting microscope supported by micro-CT, light, confocal and electron microscopy, and immunohistochemistry. Salivary biochemistry was assessed using NMR spectroscopy. The oral microbiome was evaluated using culture-based and molecular methods, and the transmissibility of dysbiosis was assessed using co-caging and microbial transfer experiments into previously germ-free recipient mice. We demonstrate that experimental kidney disease causes a reproducible reduction of alveolar bone height, without gingival inflammation or overt hyperparathyroidism but with evidence of failure of bone formation at the periodontal crest. We show that kidney disease alters the biochemical composition of saliva and induces progressive dysbiosis of the oral microbiota, with microbial samples from animals with kidney disease displaying reduced overall bacterial growth, increased alpha diversity, reduced abundance of key components of the healthy oral microbiota such as Streptococcus and Rothia, and an increase in minor taxa including those from gram-negative phyla Proteobacteria and Bacteroidetes. Co-housing diseased rats with healthy ones ameliorates the periodontal disease phenotype, whilst transfer of oral microbiota from mice with kidney disease causes periodontal disease in germ-free animals with normal kidney function. We advocate that periodontal disease should be regarded as a complication of kidney disease, initiated by oral dysbiosis through mechanisms independent of overt inflammation or hyperparathyroidism.

Exploring Bioinformatics-Driven Approaches for Enhanced Diagnosis of Chlamydia trachomatis Infections: Analysis of Target Proteins

Chlamydia trachomatis, a major cause of sexually transmitted infection, poses a range of symptoms including genital discharge, pain during urination, and abdominal pains in women, and can lead to serious health complications such as pelvic inflammatory diseases, infertility, and ectopic pregnancy if left untreated. The need for rapid and accurate detection is imperative so prompt treatment and control of the disease can be achieved. This study conducted an immunoinformatic analysis of proteins of Chlamydia trachomatis (incA, hctA, ompA, omcB, rpoB, and HSP60) for the development of a lateral flow assay-based diagnostic test. Detailed in silico evaluation of selected proteins from publicly available genomic databases was conducted to evaluate their suitability as targets for lateral flow assay-based detection. The series of tests included antigenicity, toxicity, solubility, physicochemical characteristics and molecular docking of the derived constructs, and protein sequence. Chimeric construct was derived from the prediction of linear B cell epitopes, helper T cell major histocompatibility complex II binding epitopes, and IL4 and IL10 inducers using bioinformatic tools at standard thresholds. With a Ramachandra’s score of 95.4% and Z-score of -5.1, results indicate that the construct efficacy is high in potential to provide extreme specificity and sensitivity for the detection of Chlamydia trachomatis in clinical samples as compared to traditional culture-based methods using nucleic acid amplification, hereby providing a quicker and more accurate diagnostic tool for Chlamydia trachomatis infection. Findings offer valuable data for the development of a rapid and reliable diagnostic point-of-care test kit for Chlamydia trachomatis that allows for drastic reduction in clinical wait time and treatment.

Exploring Bioinformatics-Driven Approaches for Enhanced Diagnosis of Chlamydia trachomatis Infections: Analysis of Target Proteins

Chlamydia trachomatis, a major cause of sexually transmitted infection, poses a range of symptoms including genital discharge, pain during urination, and abdominal pains in women, and can lead to serious health complications such as pelvic inflammatory diseases, infertility, and ectopic pregnancy if left untreated. The need for rapid and accurate detection is imperative so prompt treatment and control of the disease can be achieved. This study conducted an immunoinformatic analysis of proteins of Chlamydia trachomatis (incA, hctA, ompA, omcB, rpoB, and HSP60) for the development of a lateral flow assay-based diagnostic test. Detailed in silico evaluation of selected proteins from publicly available genomic databases was conducted to evaluate their suitability as targets for lateral flow assay-based detection. The series of tests included antigenicity, toxicity, solubility, physicochemical characteristics and molecular docking of the derived constructs, and protein sequence. Chimeric construct was derived from the prediction of linear B cell epitopes, helper T cell major histocompatibility complex II binding epitopes, and IL4 and IL10 inducers using bioinformatic tools at standard thresholds. With a Ramachandra’s score of 95.4% and Z-score of -5.1, results indicate that the construct efficacy is high in potential to provide extreme specificity and sensitivity for the detection of Chlamydia trachomatis in clinical samples as compared to traditional culture-based methods using nucleic acid amplification, hereby providing a quicker and more accurate diagnostic tool for Chlamydia trachomatis infection. Findings offer valuable data for the development of a rapid and reliable diagnostic point-of-care test kit for Chlamydia trachomatis that allows for drastic reduction in clinical wait time and treatment.

Application of metagenomic next-generation sequencing in the diagnosis of infectious diseases.

Metagenomic next-generation sequencing (mNGS) is a transformative approach in the diagnosis of infectious diseases, utilizing unbiased high-throughput sequencing to directly detect and characterize microbial genomes from clinical samples. This review comprehensively outlines the fundamental principles, sequencing workflow, and platforms utilized in mNGS technology. The methodological backbone involves shotgun sequencing of total nucleic acids extracted from diverse sample types, enabling simultaneous detection of bacteria, viruses, fungi, and parasites without prior knowledge of the infectious agent. Key advantages of mNGS include its capability to identify rare, novel, or unculturable pathogens, providing a more comprehensive view of microbial communities compared to traditional culture-based methods. Despite these strengths, challenges such as data analysis complexity, high cost, and the need for optimized sample preparation protocols remain significant hurdles. The application of mNGS across various systemic infections highlights its clinical utility. Case studies discussed in this review illustrate its efficacy in diagnosing respiratory tract infections, bloodstream infections, central nervous system infections, gastrointestinal infections, and others. By rapidly identifying pathogens and their genomic characteristics, mNGS facilitates timely and targeted therapeutic interventions, thereby improving patient outcomes and infection control measures. Looking ahead, the future of mNGS in infectious disease diagnostics appears promising. Advances in bioinformatics tools and sequencing technologies are anticipated to streamline data analysis, enhance sensitivity and specificity, and reduce turnaround times. Integration with clinical decision support systems promises to further optimize mNGS utilization in routine clinical practice. In conclusion, mNGS represents a paradigm shift in the field of infectious disease diagnostics, offering unparalleled insights into microbial diversity and pathogenesis. While challenges persist, ongoing technological advancements hold immense potential to consolidate mNGS as a pivotal tool in the armamentarium of modern medicine, empowering clinicians with precise, rapid, and comprehensive pathogen detection capabilities.

Comprehensive Study of Antibiotic Resistance in Enterococcus spp.: Comparison of Influents and Effluents of Wastewater Treatment Plants.

Background/Objectives: The spread of antibiotic resistance, particularly through Enterococcus spp., in wastewater treatment plants (WWTPs) poses significant public health risks. Given that research on antibiotic-resistant enterococci and their antibiotic-resistance genes in aquatic environments is limited, we evaluated the role of Enterococcus spp. in WWTPs by comparing the antibiotic resistance rates, gene prevalence, biofilm formation, and residual antibiotics in the influent and effluent using culture-based methods. Methods: In 2022, influent and effluent samples were collected from 11 WWTPs in South Korea. Overall, 804 Enterococcus strains were isolated, and their resistance to 16 antibiotics was assessed using the microdilution method. Results: High resistance to tetracycline, ciprofloxacin, kanamycin, and erythromycin was observed. However, no significant differences in the overall resistance rates and biofilm formation were observed between the influent and effluent. Rates of resistance to ampicillin, ciprofloxacin, and gentamicin, as well as the prevalence of the tetM and qnrS genes, increased in the effluent, whereas resistance rates to chloramphenicol, florfenicol, erythromycin, and tylosin tartrate, along with the prevalence of the optrA gene, decreased. E. faecium, E. hirae, and E. faecalis were the dominant species, with E. faecalis exhibiting the highest resistance. Conclusions: Our results suggest that WWTPs do not effectively reduce the rates of resistant Enterococcus spp., indicating the need for continuous monitoring and improvement of the treatment process to mitigate the environmental release of antibiotic-resistant bacteria.

Potential Impact of Rapid Molecular Microbiologic Diagnosis for Mechanically Ventilated Children in Intensive Care With Suspected Pneumonia

Background: Lower respiratory tract infections (LRTIs) remain a leading cause of community-acquired and nosocomial infection in children and a common indication for antimicrobial use and intensive care admission. Determining the causative pathogen for LRTIs is difficult and traditional culture-based methods are labor- and time-intensive. Emerging molecular diagnostic tools may identify pathogens and detect antimicrobial resistance more quickly, to enable earlier targeted antimicrobial therapy. Methods: This is a single-center, prospective observational laboratory study evaluating the use of the Biofire FilmArray pneumonia panel (FA-PP) (BioFire Diagnostics, Salt Lake City, UT) for bronchoalveolar lavage specimens from mechanically ventilated children admitted with suspected or presumed pneumonia. We aimed to determine its feasibility and utility for identifying pathogens, antimicrobial resistance and its potential influence on antibiotic prescribing. Results: We analyzed 50 samples taken from 41 children with a median age of 6 months. Positive agreement between culture and FA-PP was 83% and negative agreement was 76%. Agreement between FA-PP (mecA/C or MREJ) and culture was high for methicillin-resistant Staphylococcus aureus. In 3 cases, extended-spectrum beta-lactamase-producing Gram-negative organisms were detected by culture and not FA-PP. Hypothetically, FA-PP results would have affected antimicrobial prescribing in approximately half the cases (24, 48%). Conclusions: FA-PP is a useful adjunct to traditional culture methods in mechanically ventilated children with LRTIs and may influence clinical decision-making regarding antibiotic escalation or stewardship.

Diversity of Oomycetes From Alabama Cotton Soils Using Culture-Independent Methods.

Seedling disease management recommendations rely on comprehensive identification of the pathogens. A recent manuscript isolated oomycetes from diseased cotton (Gossypium hirsutum L.) roots to identify the species associated. However, culture-dependent surveys may miss species because of the microbiological medium and work required to isolate and maintain all cultured organisms. Alternatively, culture-independent methods using PCR amplification and high-throughput sequencing can identify species' presence and relative abundance, including obligate pathogens and rare members of communities. We used culture-independent sequencing from the same cotton soils in the culture-based survey to determine the oomycete species in oomycete-containing soils. The results of the two methods were generally similar regarding the species identified. Similarly to the culture-based method, Globisporangium irregular accounted for 24% of the relative abundance and was encountered in all fields sequenced. In contrast, we identified three operational taxonomic units matching Globisporangium ultimum, but accounted for less than 0.06% of total relative abundance, potentially explaining why it was not isolated from cotton roots in the original survey. Phytophthora nicotianae was identified in soils but not at the concentrations recorded in the culture-based study. The results of this study, combined with the results of the culture-based survey, demonstrate the most comprehensive identification of oomycetes associated with cotton in the cotton belt and the oomycetes related to seedling disease. The combined results will be essential for future research into the specific pathogen species mentioned and stimulate similar research in other states.

Development of a rapid, culture-free, universal microbial identification system using internal transcribed spacer targeting primers.

The indiscriminate administration of broad-spectrum antibiotics is a primary contributor to the increasing prevalence of antibiotic resistance. Unfortunately, culture, the gold standard for bacterial identification is a time intensive process. Due to this extended diagnostic period, broad-spectrum antibiotics are generally prescribed to prevent poor outcomes. To overcome the deficits of culture-based methods, we have developed a rapid universal bacterial identification system. The platform utilizes a unique universal polymerase chain reaction (PCR) primer set that targets the internal transcribed spacer (ITS) regions between conserved bacterial genes, creating a distinguishable amplicon signature for every bacterial species. Bioinformatic simulation demonstrates that at least 45 commonly isolated pathogenic species can be uniquely identified using this approach. We experimentally confirmed these predictions on a representative set of pathogenic bacterial species. We also show that the system can determine the corresponding concentration of each pathogen. Finally, we validated performance in clinical urinary tract infection samples.

Addressing underestimation of waterborne disease risks due to fecal indicator bacteria bound in aggregates.

This study aims to identify and address significant limitations in current culture-based regulatory methods used for monitoring microbiological water quality. Specifically, these methods' inability to distinguish between planktonic forms and aggregates containing higher bacterial loads and associated pathogens may lead to a severe underestimation of exposure risks, with critical public health implications. We employed a novel methodology combining size fractionation with ALERT, an automated rapid method for comprehensive quantification of culturable fecal indicator bacteria (FIB). Our findings reveal a substantial and widespread presence of aggregate-bound indicator bacteria across various water matrices and geographical locations. Comprehensive bacterial counts consistently exceeded those obtained by traditional methods by significant multiples, such as an average of 3.4×at the Seine River 2024 Olympic venue, and occasionally up to 100×in irrigation canals and wastewater plant effluent. These results, supported by microscopic and molecular analyses, underscore a systematic bias in global water safety regulatory frameworks. Our research demonstrates the inadequacy of traditional culture-based techniques in assessing microbiological risks posed by aggregate-bound FIB and associated pathogens, particularly in water matrices affected by FIB-rich fecal particles from recent sewer overflows or sediment, which can carry higher infectious risks. Incorporating comprehensive FIB analysis techniques, including molecular methods and rapid culture-based approaches as shown in this study, offers a promising and effective solution to these risk assessment limitations.

Deciphering the dynamics and trophic mode distribution of the leaf spot-associated fungal community of eggplant (Solanum melongena L.)

The invasion of phytopathogens impacts the composition and associations of the internal microbial inhabitants. Leaf spot is one of the most devastating diseases in eggplant var. Mattu Gulla which is unique in terms of geographic indication (GI) status. Leaf spot samples (asymptomatic and symptomatic) were collected to characterize the fungal community associated with them using culture-based and next-generation ITS rRNA-based metabarcoding approaches. Both methods showed that Ascomycota and Basidiomycota were the predominant phyla in both groups. In the asymptomatic group, Didymosphaeriaceae, Pleosporaceae, Trichomeriaceae, and Capnodiaceae were the most differentially abundant families. In contrast, Phaeosphaeriaceae, Pleosporaceae, Didymellaceae, Rhynchogastremataceae, and Bulleribasidiaceae were the most differentially abundant families in the symptomatic group. At the genus level, Cladosporium was the most differentially abundant genus in the asymptomatic group. In the symptomatic group Alternaria, Remotididymella, Vishniacozyma, Bulleribasidium, Occultifur, Epicoccum, and Loratospora were the abundant genera. The pathotroph-saprotrophic mode was the most common mode identified in both groups, with an increased abundance in the symptomatic group. Seven fungal families and two genera were identified as common according to the culture-based method and NGS analysis based on ITS rRNA metabarcoding. Our study indicated that the composition of the core microbial community varies with plant health status, and a combination of culturable and next-generation ITS rRNA-based metabarcoding approaches could be a reliable option for obtaining a detailed understanding of plant-associated fungal communities.

Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning

BackgroundRapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based methods are time-consuming, while MALDI-TOF MS is expensive. The Raman spectroscopy, due to its relatively cost-effectiveness, offers a promising alternative for bacterial identification. However, its clinical utility still requires further validation. MethodsIn this study, the artificial intelligent Raman detection and identification system (AIRDIS) was implemented to identify bacterial species, including Staphylococcus aureus (n = 1,290), Enterococcus faecium (n = 1,020), Klebsiella pneumoniae (n = 1,366), Pseudomonas aeruginosa (n = 1,067), and Acinetobacter baumannii (n = 811). Raman spectra were collected, preprocessed, and analyzed by machine learning (ML). ResultsAfter training on 24,420 Raman spectra from 1,221 isolates and testing on 4,333 isolates, the AIRDIS demonstrated an area under the curve (AUC) of 0.99 for Gram classification, with accuracies of 97.64% for Gram-positive bacteria and 98.86% for Gram-negative bacteria. Spectral differences between Gram-positive and Gram-negative bacteria were linked to structural variations in their cell walls, such as peptidoglycan and lipopolysaccharides. At the species level, S. aureus, E. faecium, K. pneumoniae, P. aeruginosa, and A. baumannii were identified with high accuracy, ranging from 94.76% to 96.88%, with all species achieving an AUC of 0.99. ConclusionsValidation with a large number of clinical isolates demonstrated Raman spectroscopy combined with ML excels in identification of five bacterial species associated with multidrug resistance. This finding confirms the clinical utility of the system while laying a solid foundation for the future development of antimicrobial resistance prediction models.

Poultry farms as a One health priority for Aspergillus section Fumigati surveillance

Abstract In poultry production environments, particulate matter (PM) is considered one of the harmful air pollutants [2]. Considering potential biological pathogens, the genus Aspergillus in particular, Aspergillus fumigatus, is a widely recognized human and animal pathogen [3]. Hot-humid farm environments could contribute to the proliferation of these fungi [4]. Therefore, poultry production workers, besides animals [4], may be at a higher risk of inhaling fungi spores [4,5]. This study aimed to determine particulate matter (PM) contamination and analyze the prevalence of Aspergillus section Fumigati in air samples from poultry pavilions. Indoor and outdoor air samples were taken in the 1st, 2nd and 3rd weeks of birds growth cycle (n = 58). Air was collected through the impaction method (MAS-100) onto polycarbonate filters, set at 100 L/min, for 5 min. Followed by DNA extraction, and real-time PCR detection of Aspergillus section Fumigati. For PM evaluation, portable direct-reading equipment (Lighthouse, model 3016 IAQ) was used. Particle concentrations were measured (n = 67) at five distinct fractions during 5 min. Considering particles fractions, PM10 and PM5 where prevalent in 1st (58% PM10; 28% PM5), 2nd (63% PM10; 26% PM5) and 3rd (64% PM10; 25% PM5) weeks. These results evidence that coarse particles (between 2.5 and 10 µm) are the most relevant PM present in this setting. Regarding fungal detection, Aspergillus section Fumigati was widespread inside poultry pavilions (62%, 26 out of 42), highlighting this environment has a potential reservoir of microbial pathogens [7]. Furthermore, PM can act as a host of biological fragments [6]. Overall, in addition to molecular analysis, future studies should consider using culture-based methods to assess the viability of pathogenic microorganisms and their infection potential [3]. From a One Health perspective, quantitative and qualitative research is required to fully understand this environment. Key messages • This study evidence that coarse particles (between 2.5 and 10 µm) are the most relevant PM present and Aspergillus section Fumigati is widespread in poultry pavilions. • A One health intervention should be considered in future studies.

Application of a metatranscriptomics technology, CSI-Dx, for the detection of pathogens associated with prosthetic joint infections

Preoperative identification of causal organism(s) is crucial for effective prosthetic joint infection treatment. Herein, we explore the clinical application of a novel metatranscriptomic (MT) workflow, CSI-Dx, to detect pathogens associated with prosthetic joint infection. MT provides insight into transcriptionally active microbes, overcoming limitations of culture-based and available molecular methods. This study included 340 human synovial fluid specimens subjected to CSI-Dx and traditional culture-based methods. Exploratory analyses were conducted to determine sensitivity and specificity of CSI-Dx for detecting clinically-relevant taxa. Our findings provide insights into the active microbial community composition of synovial fluid from arthroplasty patients and demonstrate the potential clinical utility of CSI-Dx for aiding prosthetic joint infection diagnosis. This approach offers potential for improved sensitivity and acceptable specificity compared to synovial fluid culture, enabling detection of culturable and non-culturable microorganisms. Furthermore, CSI-Dx provides valuable information on antimicrobial resistance gene expression. While further optimization is needed, integrating metatranscriptomic technologies like CSI-Dx into routine clinical practice can revolutionize prosthetic joint infection diagnosis by offering a comprehensive and active snapshot of associated pathogens.