Journal of Microbiological Methods

Measuring the growth rate of filamentous fungi is an essential phenotype assay in fungal biology, enabling the comparison of nutrient-related fitness metrics across various isolates, species and genera. Conventional methods are time consuming and labor intensive, which prohibits the adaptation and implementation of high-throughput phenotyping. Here, we suggest a high-throughput methodological pipeline to study fungal growth on solid media combining the use of 24-well plates, an automated image acquisition system, and human assisted deep learning analysis of acquired images. Training a deep learning model through an iterative process - with continuous feedback and corrective annotations - enabled the development of a satisfying model that automatically segments pixels belonging to either fungus or background within a few hours. We evaluated this deep learning model by applying it to two test sets: First, a set of 336 images was used to validate the results by comparison with manual measurements. We demonstrate that the automated segmentation approach provides robust estimation of fungal growth not significantly different to manually segmented data. Second, a larger test set consisting of 2,016 images was used to illustrate the scalability of the model. After training the model for less than two hours, the deep learning model segmented the entire image data set automatically within minutes. The presented method is easily scalable and adjustable to other fungi and growth morphologies, due to the interactive training. Moreover, by combining 24-well plates and automatic image acquisition, measurements can be sped up as growth is detected across a smaller surface area than a standard six or nine cm diameter petri dish. The proposed methodological pipeline thus offers a new tool for estimating fungal growth rates, which can accelerate measurements, reduce bias, and increase throughput.

This dual-center study evaluated the impact of artificial intelligence (AI) on urine culture turnaround times in Canadian diagnostic laboratories employing full microbiology laboratory automation. Data were collected before and after the implementation of PhenoMATRIX (PM), an AI-based software designed to support culture sorting and result interpretation. In both a low-volume tertiary care hospital and a high-volume community laboratory, PM reduced the time to final culture reporting, with decreases of approximately 1.5 hours and 3.9 hours, respectively. Implementation of PM+, which automatically releases defined results to patient charts, further improved turnaround time. These findings indicate that microbiology laboratories with full laboratory automation can achieve further improvements in turnaround time by integrating AI-culture assessment and results release.

Accurate quantification of fungi is important for a myriad of applications but remains challenging. Previously, we demonstrated that an approach called the adenine-HPLC method can quantify bacteria, including those with aggregating properties that are difficult to quantify using conventional methods, by measuring cellular adenine derived from DNA and converting the adenine amount to genome copy number, without being influenced by cell morphology. However, in this study, when this adenine-HPLC method was applied to the quantification of budding yeast as a model fungus, accurate measurement proved impossible. This limitation was attributed to adenine release from other adenine-containing biomolecules, such as RNA and ATP, and we therefore developed a method that suppresses adenine release from these molecules. This method involves reducing the temperature of the acid treatment and prewashing the cells before acid treatment. In addition, we incorporated a process that corrects for the naturally occurring free adenine level as background during total adenine measurement. The improved adenine-HPLC method based on these modifications enables accurate quantification of budding yeast using genomic DNA content in whole cells as the quantification unit.

AbstractEmergomyces africanus is a thermally dimorphic fungal pathogen endemic to Southern Africa which can cause fatal systemic infections in persons with advanced HIV disease. Its mechanisms of pathogenesis are not well understood. Characterisation of virulence traits in this pathogen requires appropriate molecular tools for genetic manipulation. Molecular technologies developed for the transformation of H. capsulatum were adapted for use in E. africanus. Agrobacterium-mediated transformation was used to generate a reporter strain expressing green fluorescent protein (GFP). The E. africanus GFP reporter strain facilitated the study of yeast interaction with macrophages in vitro and allowed the identification of infected phagocyte cell types in the mouse lung by flow cytometry. E. africanus could also maintain episomal plasmids with telomere-like sequences, to introduce expression constructs without genome modification. Using this plasmid system, RNA interference constructs were used to knock down the expression of cell wall (1,3)-glucan by targeting the transcripts of the -glucan synthase (AGS1). An episomal CRISPR/Cas9 system was evaluated for E. africanus, which effectively disrupted GFP in a reporter strain and enabled the generation of a URA5 uracil auxotroph. These tools and strains will facilitate future studies to elucidate the mechanisms of pathogenesis of E. africanus. ImportanceEmergomyces africanus is an opportunistic fungal pathogen affecting persons with advanced HIV disease in South Africa. The biology and pathogenesis of E. africanus are not well understood, as the importance of the disease caused by this fungus (emergomycosis) has only been recognised in recent years and molecular studies have been impaired by the lack of genetic technologies. In this work, we describe tools and methods for the genetic modification of this pathogen, which will accelerate future studies investigating how the fungus causes disease in the human host. These essential tools include (1) the ability to create fluorescent reporter strains, such as the green fluorescent protein E. africanus strain described here, which facilitates tracking the spread of the fungus during infection and enhances microscopy studies, (2) methods for knocking down gene expression in E. africanus, and (3) the permanent disruption of genes through CRISPR/Cas9 gene editing.

BackgroundThe environment in which a fungus grows can directly influence their development, transmission, and pathogenic potential. This environment encompasses factors like nutrient availability, biotic and abiotic stressors, as well as host-derived chemical cues. In fungal pathogens, where conidia act as the infectious agents, the environment impacts the quantity and quality of these spores, thereby aOecting their ability to infect and kill hosts. In the present study, we investigated the effect of host-derived medium types on various phenotypes, including spore production, growth rate, and virulence in two entomopathogenic fungi, Metarhizium acridum and Metarhizium brunneum. Three medium types derived from insect material were compared to a standard laboratory medium. ResultsConidia produced on the insect-derived media exhibited enhanced sporulation and reduced time to sporulation, while conidial germination and maximum growth rate were comparable across medium types, suggesting that some of the medium-induced phenotypic effects were transient. Notably, conidia derived from two of the insect medium types demonstrated higher virulence, indicating that host-derived cues may prime virulence. ConclusionThese results highlight that the composition of growth substrates can regulate fungal reproductive strategies and virulence, with implications for developing high-throughput phenotyping and for the biotechnological optimization of mass production and efficacy of entomopathogenic fungi in biological control applications. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/711814v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@189013eorg.highwire.dtl.DTLVardef@1b0cedborg.highwire.dtl.DTLVardef@dccb4eorg.highwire.dtl.DTLVardef@1a77895_HPS_FORMAT_FIGEXP M_FIG C_FIG

Manual colony counting remains the rate-limiting, operator-dependent step in culture-based food microbiology quality control (QC). Automated colony analysis using machine learning (ML) offers the potential to standardise, accelerate, and improve the traceability of this process. However, systematic multi-method validation data for AI-based platforms against recognised international standards remain scarce. We conducted a prospective, multi-study validation of the Reshape Smart Incubator which is an automated imaging and ML-based colony analysis system, across eight ISO microbiological reference methods. In total, 887 plates were analysed, spanning qualitative (presence/absence) detection of Listeria spp. (ISO 11290-1) and Salmonella spp. (ISO 6579), and quantitative enumeration of total viable count (ISO 4833), Bacillus cereus (ISO 7932), Enterobacteriaceae (ISO 21528), coagulase-positive Staphylococci (ISO 6888), yeasts and moulds (ISO 21527), and lactic acid bacteria (ISO 15214). Automated results were benchmarked against the consensus of three or more trained technicians. The platform achieved 100% agreement with manual assessment for all both qualitative detection methods (ISO 11290-1, ISO 6579) with zero false positives and zero false negatives. For quantitative enumeration, agreement ranged from 92.97% (ISO 15214, n=122, using ISO-aligned {+/-}10%/>30 CFU thresholds) to 98.46% (ISO 21528, n=130). Where discrepancies occurred, they largely coincided with plates showing high inter-technician variability. Precision testing demonstrated a coefficient of variation of 5.88% and a mean standard deviation of 0.44 CFU for low-count plates. This study presents a comprehensive multi-ISO validation of an AI-based colony analysis system to date. The AI models demonstrated performance comparable to or exceeding that of trained human technicians across a broad range of microbiological targets, agar types, and colony morphologies, thereby supporting their use as a validated and traceable alternative to manual plate reading in accredited food microbiology quality control laboratories.

The study presented here shows Biofilm quantification in microtiter plates in strains of Candida auris and Candida albicans evaluated by means of Crystal violet, MTT, ATP-Luminescence and NBTZ/BCIP assays. The results showed significant differences in biofilm formation between Candida auris and Candida albicans but also within Candida auris outbreak strains in contrast to Candida auris DSM 21092 reference strain.

Vancomycin-resistant Enterococcus faecium (VREfm) is a major nosocomial pathogen, with antibiotic resistance mediated by the vanA and vanB operons. Rapid and accurate detection of antibiotic resistance is critical for the timely treatment of bacteremia and sepsis. Although imaging-based approaches using fluorescence in situ hybridization (FISH) provide a potential diagnostic solution, detecting mRNAs of antibiotic resistance genes (ARGs) in individual cells remains particularly challenging due to their low copy number and transient expression. Here, we present a peptide nucleic acid (PNA)-FISH method for direct detection of vanA- and vanB-associated resistance in individual VREfm cells. A universal probe targeting the conserved region across vancomycin resistance genes and a set of probes exclusively targeting the vanB gene were designed. The universal probe showed increased fluorescence in the vanA-genotype strain upon vancomycin or teicoplanin treatment, and in the vanB-genotype strain upon vancomycin treatment. In contrast, vanB-specific probes showed increased fluorescence exclusively from the vanB-genotype strain upon vancomycin treatment, confirming their specificity to the vanB gene. Efficient cellular penetration and strong hybridization of PNA probes enabled efficient and accurate detection of antibiotic-resistant bacterial cells, even under a wide-field fluorescence microscope. No detectable signals above background were observed in other major bacterial species associated with bacteremia and sepsis. These findings demonstrate robust detection of antibiotic-resistant cells in mixed microbial populations. When integrated with microbe-capturing techniques, this method may support culture-free detection of antibiotic resistance without nucleic acid amplification or sequencing, with the potential to reduce diagnostic turnaround time.

Chitosan is an oligosaccharide derived from chitin that is protonated at acidic pH to form a polycation. Its positive charge promotes the interaction with negatively charged components of the yeast cell surface, which has been associated with increased cell permeability and growth inhibition. In this study, we investigated the interaction of chitosan with the cell surface and its permeabilizing capacity in three yeast species displaying distinct susceptibility profiles, Saccharomyces cerevisiae, Candida albicans and Debaryomyces hansenii. We evaluated the correlation between differential susceptibility and chitosan association at the cell surface, as well as cell permeabilization, by integrating growth analyses with surface-binding assays, including FITC-conjugated chitosan to monitor surface association and cellular integration over time, and ultrastructural examination by transmission electron microscopy (TEM). Our results showed that chitosan exhibited varying effects on the growth and permeability of each yeast strain, with D. hansenii being the most susceptible. Furthermore, we observed the incorporation of chitosan onto the cell surface and confirmed its role as a permeabilizing agent. Finally, we used chitosan-induced permeabilization as a method to measure the activity of selected enzymes in situ, demonstrating its potential for studying metabolic functions in permeabilized yeast cells. Overall, our findings establish chitosan as a strain-dependent antifungal agent and a useful tool for functional biochemical analyses in yeast.

Trichophyton mentagrophytes genotype VII (TmVII) is an emerging sexually transmitted dermatophyte that causes skin infections characterized by inflammatory, erythematous-squamous, painful, and persistent lesions. This genotype is part of the T. interdigitale/T. mentagrophytes Species Complex (TiTmSC), which comprises 28 genotypes. To enable rapid and specific differentiation of TmVII from other genotypes, a real-time polymerase chain reaction (rt-PCR) assay was developed targeting three unique single-nucleotide polymorphisms in the ITS1 region of TmVII. Assay specificity was further improved by introducing an additional mismatch at the 3 ends of both forward and reverse primers. The rt-PCR assay demonstrated high sensitivity, with a detection limit of 0.0002 ng of TmVII genomic DNA. The assay was highly specific, with no cross-reactivity observed with either closely or distantly related fungal pathogens when a cycle threshold (Ct) cutoff of 37 was applied. Among 497 mold isolates tested, 47 were confirmed as TmVII by rt-PCR, and the results were fully concordant with conventional ITS-PCR/Sanger sequencing. The rt-PCR assay demonstrated high sensitivity, specificity, reproducibility, and speed, with a turnaround time of one day after DNA extraction, compared with seven to ten days for Sanger sequencing. The first rapid molecular assay developed using TaqMan chemistry for TmVII identification is expected to enhance patient care and support infection control measures.

Antimicrobial resistance remains a global existential threat. Given that antimicrobial therapy commonly starts before pathogen identification, rapid and scalable methods capable of determining effective antimicrobial compounds are needed. In this paper, we demonstrate a 2 x 2 array of parallelised microscopes that uses low numerical aperture (NA=0.25) detection optics and LED excitation to determine bacterial viability based on their fluorescence response to an electrical stimulus. Following a 2-hour incubation, the fluorescent viability readout requires less than one minute. We use K-means clustering to classify pixels in a time lapse sequence of widefield fluorescence images and extract changes seen within bacterial clusters. We demonstrate sufficient sensitivity to measure fluorescence changes after electrical stimulation in a bacterial monolayer. To capture these subtle fluorescence changes at high signal-to-background ratios, we place a limit on the minimum optical density of the bacterial sample. This novel approach is scalable to 96-well formats using a suitable consumable electrode array.

The Klebsiella pneumoniae species complex (KpSC) is a clinically important group of closely related pathogens associated with invasive infections. The complex comprises seven closely related members, which are often reported as K. pneumoniae, particularly in resource-limited settings. Accurate differentiation of KpSC members remains challenging because routine laboratory methods lack sufficient resolution, and approaches like mass spectrometry and whole genome sequencing (WGS) are not widely available. Consequently, the epidemiology and clinical significance of non-K. pneumoniae members of the KpSC remain underrecognized. We developed a conventional multiplex mismatch amplification mutation assay (MAMA) PCR targeting species- and subspecies-specific single-nucleotide polymorphisms in the housekeeping gene rpoB, with six primer sets for differentiation of common KpSC members. The assay was validated against 49 genomically characterized clinical isolates, after which 179 wastewater-derived isolates provisionally identified as Klebsiella spp. by standard microbiological methods were tested. Of these, 174 were assigned to specific KpSC members by the assay, while 5 produced inconclusive amplification patterns. A subset of 16 environmental isolates was selected for WGS, including four of the five inconclusive isolates. All environmental isolates with interpretable MAMA PCR patterns were concordant with WGS. The four inconclusive environmental isolates were identified as Enterobacter spp. Overall, comparison of MAMA PCR with WGS showed 100% sensitivity and 100% specificity for all tested targets, and the total cost was approximately US$1. This rpoB-based multiplex MAMA PCR provides a simple, accurate, and low-cost approach for differentiation of KpSC members in routine laboratories and may support improved identification and surveillance in resource-limited settings. ImportanceThe Klebsiella pneumoniae species complex (KpSC) has seven members but is often reported as a single organism in routine laboratories, masking clinically and epidemiologically important diversity. As a result, the contribution of non-K. pneumoniae KpSC members to human and environmental microbiology remains poorly defined, especially in low-resource settings. We developed a conventional multiplex mismatch amplification mutation assay (MAMA) PCR based on discriminatory rpoB single nucleotide polymorphisms for differentiation of common KpSC members using standard PCR and agarose gel electrophoresis. The assay demonstrated 100% sensitivity and 100% specificity against whole-genome sequencing and excluded non-Klebsiella environmental isolates initially identified as Klebsiella pneumoniae using standard microbiological procedures. With an estimated per-test cost of about US$1, this method offers an affordable and scalable option for laboratories seeking more accurate KpSC identification and improved surveillance.

Yarrowia lipolytica is a yeast of industrial interest exhibiting remarkable lipolytic and proteolytic capacities, with a high potential for white biotechnology applications. This yeast can be isolated from a wide range of natural, polluted or anthropogenic environments, including various food products. The present study aims to increase the data on Y. lipolytica phenotypic diversity by evaluating the growth parameters and secreted enzymatic activities of 28 wild-type Y lipolytica (and Yarrowia sp.) strains isolated from various environments across 10 countries. These data could facilitate the selection of appropriate strains for specific research purposes, particularly when wild-type strains are prioritized over genetically engineered ones, like for food-related applications. Notably, strain SWJ-1b exhibited an outstanding combination of favourable characteristics, with optimum (or near) performances for both growth and enzymatic parameters.

Species-level Candida identification can inform antifungal management, but reliable identification platforms remain inaccessible in many clinical microbiology laboratories, whereas phase contrast microscopy -- a common feature of routine laboratory microscopes -- is widely available. We asked whether this ubiquitous optical tool, combined with a consumer smartphone and deep transfer learning, could provide a feasible low-cost approach for preliminary Candida species discrimination. Fifteen clinical isolates of four species (C. albicans, C. glabrata, C. tropicalis, C. krusei) were collected from a single clinical microbiology laboratory and imaged using a consumer-grade smartphone coupled to a standard phase contrast microscope. Suspensions in human serum were imaged immediately after preparation (T0) and after 2-hour incubation at 37{degrees}C (T2). Pretrained vision backbone architectures were evaluated as fixed feature extractors under strict Leave-One-Strain-Out cross-validation. The best-performing model -- EfficientNet-B0 embeddings with a Linear Support Vector Machine applied to T2 images -- achieved an apparent internally cross-validated strain-level balanced accuracy of 0.833 and an overall strain accuracy of 86.7% (13/15 strains correctly classified). C. albicans, C. glabrata, and C. tropicalis were each identified with 100% recall. Both misclassified strains belonged to C. krusei -- the species with the smallest panel representation (n=3 strains) -- with misclassification attributable to limited strain diversity and suboptimal image quality. These findings demonstrate promising feasibility for preliminary image-based Candida species discrimination from smartphone-acquired phase contrast microscopy images, and support further evaluation in larger, externally validated strain collections.

In heterogeneous environments, the hyphae of filamentous fungi and oomycetes can facilitate the dispersal of other microorganisms. The use of these "fungal highways" (FH) is regulated by both physical and biological factors with their interplay resulting in variable capabilities of different microbes to establish FH. Several devices have been developed to test the movement of bacteria across mycelium. However, these methods are usually time-consuming and cannot be applied either at a large scale or in a high throughput format. In this study, we developed 3D-printed experimental devices that physically separate two environments while allowing hyphal networks to act as bridges for bacterial movement. The final design allows for the simultaneous testing of up to 10 pairs and the inclusion of any culturing media. With these devices, we investigated how fungal-bacterial pairing, nutrient conditions, and inoculation strategies influence FH formation. Bacterial transport was limited in nutrient-rich media but increased under poorer nutrient conditions, consistent with enhanced exploratory growth of the mycelium. Both cis- and trans-inoculation supported FH formation, although bacterial arrival was delayed in the absence of co-inoculation. The devices were used to demonstrate that transport of bacteria by FH was relevant for the colonization of a natural substrate. Finally, we established a novel in planta assay to evaluate FH formation during host colonization. This assay demonstrated that Fusarium graminearum can transport bacteria during wheat spike colonization. Together, these results provide accessible, scalable tools to study hyphal-mediated bacterial dispersal and highlight the combined role of biological specificity and nutrient context in the establishment of FH. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/719220v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1cf39aforg.highwire.dtl.DTLVardef@1d41e6corg.highwire.dtl.DTLVardef@1196637org.highwire.dtl.DTLVardef@85cc6c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Yeasts ability to invade surfaces has important implications for infections and food contamination. Invasive growth in yeast is influenced by genetic and environmental factors. In this exploratory study, we investigated the effects of sodium sulfide, gene deletions, and environmental conditions on the invasive behaviour of the wine yeast strain AWRI 796. Sodium sulfide enhanced invasion in the (parent) AWRI 796 strain under nitrogen-limiting conditions, although its effect was obscured by experimental variability and pre-culture conditions. Genetic factors had a major effect on the overall invasive phenotype, with deletion of key genes suppressing invasion. Most gene-deletion mutants did not significantly affect how the colony responded to sulfide. In addition to sulfide and genotype, environmental conditions also influenced invasive behaviour. The pre-2xSLAD pre-culture condition was best for detecting sulfide-induced growth, and later plate washing time and decreased nutrient levels enhanced invasiveness. Our experimental design and findings provide a framework for understanding the determinants of yeast invasiveness, which may inform future studies on filamentous yeast behaviour.

Soil-transmitted helminths (STHs) pose significant challenges to public health in endemic areas, necessitating reliable methods for their detection. Shotgun metagenomics enables simultaneous detection of STHs and microbes in a sample without prior knowledge of what is present. However, validation of shotgun metagenomics with known infection intensity or across different sequencing platforms has not been carried out for eukaryote parasites including STHs, and false positives remain a pervasive issue. We validated shotgun metagenomics as a method of STH detection in faecal samples. Using the Strongyloides ratti laboratory model of a STH infection we investigated how analytical methods (nucleotide-nucleotide matching, nucleotide-protein matching, marker gene detection, mitochondrial mapping), infection intensity and sequencing technology (short-read vs. long-read) affects sensitivity and specificity of detection. S. ratti was accurately detected at a standard laboratory dose, but low intensity infections were more difficult to detect. Only mitochondrial sequence mapping was 100% accurate at identifying S. ratti with no false positives. Overall, short-read outperformed long-read sequencing methods. We applied the same analytical methods to human faecal samples with confirmed infections for at least one of four STHs. Mitochondrial sequence mapping was also the most effective method for detecting STHs in human faecal samples, detecting 100% of Necator americanus and 92% of Ascaris spp. infections, but could not reliably detect STHs where DNA levels are expected to be low or variable. In conclusion, mitochondrial mapping was the most effective method of detection for sensitivity and specificity in both the laboratory system and human faecal samples. Our findings indicate that shotgun metagenomics should be approached cautiously using validated methods, particularly when infection intensity or DNA levels are expected to be low. Author SummarySoil-transmitted helminths (STH) such as the parasite Strongyloides, are important gastrointestinal parasites of humans and livestock. Accurate methods of detection for diagnostics and monitoring are important to implement suitable control and treatment strategies. Here we validate a shotgun metagenomics approach, where all DNA in a sample is sequenced, for detecting STH in faecal samples using a Strongyloides laboratory model for infection. Strongyloides was reliability detected in faecal samples at higher infection levels, but mitochondrial genome mapping of the sequences was the only analytical method that reliably detected Strongyloides at lower infections levels. These results were reflected in stool samples from humans infected with STH, where mitochondrial mapping was also the most reliable method. However, species that were associated with low levels of parasite material or DNA in the faeces including Strongyloides stercoralis, were more difficult to detect. We compared two sequencing methods: short-read Illumina and long-read Oxford Nanopore Technologies, but short-read outperformed long-read shotgun metagenomics. Contamination of bacteria sequences in parasite genome assemblies was problematic for analysis and contributed to false positive results. Future work should focus on specific targeting of eukaryote DNA either at the laboratory or bioinformatic stage to improve STH detection further.

Rock-inhabiting fungi thrive in subaerial oligotrophic environments such as desert rocks, solar panels and marble monuments where organic carbon and nitrogen are scarce. We tested whether the rock-inhabiting fungus Knufia petricola showed a preference regarding nitrogen ([Formula] or [Formula]) and carbon (glucose or sucrose) sources and whether it was sensitive towards carbon and nitrogen limitation. As this fungus produces the carbon-rich, nitrogen-free 1,8-dihydroxynaphthalene (DHN) melanin, we tested whether a melanin-deficient mutant would be less sensitive to carbon limitation. The carbon and nitrogen concentrations were the primary predictors of growth, with a broad optimum partially explained by an optimal fungal C:N ratio. Limiting carbon or nitrogen supply decreased biomass formation, CO2 production and biofilm thickness but promoted substratum penetration through filamentous growth. The nitrogen content of the biomass was flexible within limits, increasing upon increasing nitrogen supply or decreasing carbon supply. The carbon use efficiency was fairly constant, whereas melanization correlated with a higher nitrogen content of the biomass despite melanin being nitrogen-free. In conclusion, in vitro, K. petricola switches to explorative growth under nutrient limitations, like fast-growing fungi, revealing universal fungal resource-acquisition patterns. Graphical abstract text and imageCarbon and nitrogen availability affect biofilm growth and morphology of the extremotolerant fungus Knufia petricola Abolfazl Dehkohneh, Julia Schumacher, Bastiaan J. R. Cockx, Karin Keil, Tessa Camenzind, Jan-Ulrich Kreft, Anna A. Gorbushina, Ruben Gerrits Growth of the rock-inhabiting fungus Knufia petricola was studied by varying carbon and nitrogen sources and concentrations. Overall, growth was best predicted by the carbon and nitrogen concentrations. Carbon and nitrogen limitation promoted substratum penetration through filamentous growth. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/712823v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@6d98bdorg.highwire.dtl.DTLVardef@146aac5org.highwire.dtl.DTLVardef@757fa8org.highwire.dtl.DTLVardef@ff709_HPS_FORMAT_FIGEXP M_FIG C_FIG

Considerable effort has focused on identifying alternatives to mouse models in research studies. In the field of bacterial pathogenesis, Galleria mellonella and epithelial cell lines have been widely used for this purpose, but the concordance of these models with mice remains unclear. To begin to address this knowledge gap, we used 105 clinical isolates of Pseudomonas aeruginosa for which the virulence had been previously determined in a mouse bacteremia model. A semistrong correlation was observed between G. mellonella median time to 50% mortality and mouse 50% pre-lethal dose (LD50) values (Spearmans rank correlation coefficient [{rho}] = 0.75), whereas percent A549 epithelial-like cell lysis during co-culture showed a weak correlation to mouse LD50 values ({rho} = -0.47). Given the stronger correlation between G. mellonella and mouse virulence, we next examined whether G. mellonella could substitute for mice when asking questions about the virulence of large numbers of P. aeruginosa isolates. Results from mice indicated that isolates with resistance to more antibiotics were significantly less virulent, and the use of G. mellonella identified the same inverse correlation. Furthermore, both models found no evidence for the existence of hypervirulent clonal lineages. In particular, isolates belonging to sequence types defined as high-risk clones were not consistently more virulent than other isolates, despite the known association of high-risk clones with poor clinical outcomes. These findings suggest that G. mellonella can serve as an adequate substitute for mice when addressing specific population-based virulence questions, although conclusions should be confirmed in mice. Author SummaryWe found that virulence measurements in a G. mellonella infection model showed a semistrong correlation with those from a mouse bacteremia model and that this insect larval model adequately detected population-level trends similarly to mice. In contrast, A549 epithelial-like cell lysis during bacterial co-culture correlated less well with mouse virulence. Together, these results support the use of G. mellonella as a scalable, low-cost, and humane first-line model for assessing P. aeruginosa virulence but also indicate that conclusions should be validated in mice.

Milk is a challenging matrix to extract sufficient microbial DNA from for downstream analysis. This study assessed fourteen DNA extraction protocols for their DNA outputs. An adaptation of the QIAGEN DNeasy PowerSoil kit, which increased initial sample volume and maintained all volume of lysate following bead beating proved most effective.