Novel Pseudomonas Species Prevent the Growth of the Phytopathogenic Fungus Aspergillus flavus.

In response to the escalating demand for sustainable agricultural methodologies, the utilization of microbial volatile organic compounds (VOCs) as antagonists against phytopathogens has emerged as a viable eco-friendly alternative. Microbial volatiles exhibit rapid diffusion rates, facilitating prompt chemical interactions. Moreover, microorganisms possess the capacity to emit volatiles constitutively, as well as in response to biological interactions and environmental stimuli. In addition to volatile compounds, these bacteria demonstrate the ability to produce soluble metabolites with antifungal properties, such as APE Vf, pyoverdin, and fragin. In this study, we identified two Pseudomonas strains (BJa3 and MCal1) capable of inhibiting the in vitro mycelial growth of the phytopathogenic fungus Aspergillus flavus, which serves as the causal agent of diseases in sugarcane and maize. Utilizing GC/MS analysis, we detected 47 distinct VOCs which were produced by these bacterial strains. Notably, certain volatile compounds, including 1-heptoxydecane and tridecan-2-one, emerged as primary candidates for inhibiting fungal growth. These compounds belong to essential chemical classes previously documented for their antifungal activity, while others represent novel molecules. Furthermore, examination via confocal microscopy unveiled significant morphological alterations, particularly in the cell wall, of mycelia exposed to VOCs emitted by both Pseudomonas species. These findings underscore the potential of the identified BJa3 and MCal1 Pseudomonas strains as promising agents for fungal biocontrol in agricultural crops.

Identification and functional analysis of novel protein-encoding sequences related to stress-resistance.

Currently, industrial bioproducts are less competitive than chemically produced goods due to the shortcomings of conventional microbial hosts. Thus, is essential developing robust bacteria for improved cell tolerance to process-specific parameters. In this context, metagenomic approaches from extreme environments can provide useful biological parts to improve bacterial robustness. Here, in order to build genetic constructs that increase bacterial resistance to diverse stress conditions, we recovered novel protein-encoding sequences related to stress-resistance from metagenomic databases using an in silico approach based on Hidden-Markov-Model profiles. For this purpose, we used metagenomic shotgun sequencing data from microbial communities of extreme environments to identify genes encoding chaperones and other proteins that confer resistance to stress conditions. We identified and characterized 10 novel protein-encoding sequences related to the DNA-binding protein HU, the ATP-dependent protease ClpP, and the chaperone protein DnaJ. By expressing these genes in Escherichia coli under several stress conditions (including high temperature, acidity, oxidative and osmotic stress, and UV radiation), we identified five genes conferring resistance to at least two stress conditions when expressed in E. coli. Moreover, one of the identified HU coding-genes which was retrieved from an acidic soil metagenome increased E. coli tolerance to four different stress conditions, implying its suitability for the construction of a synthetic circuit directed to expand broad bacterial resistance.

Metagenomic Insights for Antimicrobial Resistance Surveillance in Soils with Different Land Uses in Brazil.

Land-use conversion changes soil properties and their microbial communities, which, combined with the overuse of antibiotics in human and animal health, promotes the expansion of the soil resistome. In this context, we aimed to profile the resistome and the microbiota of soils under different land practices. We collected eight soil samples from different locations in the countryside of São Paulo (Brazil), assessed the community profiles based on 16S rRNA sequencing, and analyzed the soil metagenomes based on shotgun sequencing. We found differences in the communities' structures and their dynamics that were correlated with land practices, such as the dominance of Staphylococcus and Bacillus genera in agriculture fields. Additionally, we surveyed the abundance and diversity of antibiotic resistance genes (ARGs) and virulence factors (VFs) across studied soils, observing a higher presence and homogeneity of the vanRO gene in livestock soils. Moreover, three ß-lactamases were identified in orchard and urban square soils. Together, our findings reinforce the importance and urgency of AMR surveillance in the environment, especially in soils undergoing deep land-use transformations, providing an initial exploration under the One Health approach of environmental levels of resistance and profiling soil communities.

ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization.

BACKGROUND: Genome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools. RESULTS: Here we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present. CONCLUSIONS: Overall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.

Systems-Wide Dissection of Organic Acid Assimilation in Pseudomonas aeruginosa Reveals a Novel Path To Underground Metabolism.

The human pathogen Pseudomonas aeruginosa (Pa) is one of the most frequent and severe causes of nosocomial infection. This organism is also a major cause of airway infections in people with cystic fibrosis (CF). Pa is known to have a remarkable metabolic plasticity, allowing it to thrive under diverse environmental conditions and ecological niches; yet, little is known about the central metabolic pathways that sustain its growth during infection or precisely how these pathways operate. In this work, we used a combination of 'omics approaches (transcriptomics, proteomics, metabolomics, and 13C-fluxomics) and reverse genetics to provide systems-level insight into how the infection-relevant organic acids succinate and propionate are metabolized by Pa. Moreover, through structural and kinetic analysis of the 2-methylcitrate synthase (2-MCS; PrpC) and its paralogue citrate (CIT) synthase (GltA), we show how these two crucial enzymatic steps are interconnected in Pa organic acid assimilation. We found that Pa can rapidly adapt to the loss of GltA function by acquiring mutations in a transcriptional repressor, which then derepresses prpC expression. Our findings provide a clear example of how "underground metabolism," facilitated by enzyme substrate promiscuity, "rewires" Pa metabolism, allowing it to overcome the loss of a crucial enzyme. This pathogen-specific knowledge is critical for the advancement of a model-driven framework to target bacterial central metabolism. IMPORTANCE Pseudomonas aeruginosa is an opportunistic human pathogen that, due to its unrivalled resistance to antibiotics, ubiquity in the built environment, and aggressiveness in infection scenarios, has acquired the somewhat dubious accolade of being designated a "critical priority pathogen" by the WHO. In this work, we uncover the pathways and mechanisms used by P. aeruginosa to grow on a substrate that is abundant at many infection sites: propionate. We found that if the organism is prevented from metabolizing propionate, the substrate turns from being a convenient nutrient source into a potent poison, preventing bacterial growth. We further show that one of the enzymes involved in these reactions, 2-methylcitrate synthase (PrpC), is promiscuous and can moonlight for another essential enzyme in the cell (citrate synthase). Indeed, mutations that abolish citrate synthase activity (which would normally prevent the cell from growing) can be readily overcome if the cell acquires additional mutations that increase the expression of PrpC. This is a nice example of the evolutionary utility of so-called "underground metabolism."

Nosocomial Outbreak of Extensively Drug-Resistant (Polymyxin B and Carbapenem) Klebsiella pneumoniae in a Collapsed University Hospital Due to COVID-19 Pandemic.

We correlated clinical, epidemiological, microbiological, and genomic data of an outbreak with polymyxin B (PB)- and carbapenem-resistant Klebsiella pneumoniae during the COVID-19 pandemic. Twenty-six PB- and carbapenem-resistant K. pneumoniae were isolated from patients in the COVID-19 ICU (Intensive Care Unit), non-COVID-19 ICU (Intensive Care Unit), clinical, or surgical ward. Bacterial identification, drug susceptibility tests, and DNA sequencing were performed, followed by in silico resistance genes identification. All isolates showed extensively drug-resistant (XDR) phenotypes. Four different sequence types (ST) were detected: ST16, ST11, ST258, and ST437. Nineteen isolates were responsible for an outbreak in the ICU in September 2020. They belong to ST258 and harbored the 42Kb IncX3plasmid (pKP98M3N42) with the same genomic pattern of two K. pneumoniae identified in 2018. Twenty-four isolates carried bla-KPC-2 gene. No plasmid-mediated colistin (mcr) resistance genes were found. Eight isolates presented mgrB gene mutation. The clonal isolates responsible for the outbreak came from patients submitted to pronation, with high mortality rates in one month. XDR-K. pneumoniae detected during the outbreak presented chromosomal resistance to PB and plasmid-acquired carbapenem resistance due to KPC production in most isolates and 42Kb IncX3(pKP98M3N42) plasmid carrying blaKPC-2 was associated with ST258 isolates. The outbreak followed the collapse of the local healthcare system with high mortality rates.

PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning.

BACKGROUND: Transcription factors (TFs) are proteins controlling the flow of genetic information by regulating cellular gene expression. A better understanding of TFs in a bacterial community context may open novel revenues for exploring gene regulation in ecosystems where bacteria play a key role. Here we describe PredicTF, a platform supporting the prediction and classification of novel bacterial TF in single species and complex microbial communities. PredicTF is based on a deep learning algorithm. RESULTS: To train PredicTF, we created a TF database (BacTFDB) by manually curating a total of 11,961 TF distributed in 99 TF families. Five model organisms were used to test the performance and the accuracy of PredicTF. PredicTF was able to identify 24-62% of the known TFs with an average precision of 88% in our five model organisms. We demonstrated PredicTF using pure cultures and a complex microbial community. In these demonstrations, we used (meta)genomes for TF prediction and (meta)transcriptomes for determining the expression of putative TFs. CONCLUSION: PredicTF demonstrated high accuracy in predicting transcription factors in model organisms. We prepared the pipeline to be easily implemented in studies profiling TFs using (meta)genomes and (meta)transcriptomes. PredicTF is an open-source software available at https://github.com/mdsufz/PredicTF .

Chromatin profiling reveals heterogeneity in clinical isolates of the human pathogen Aspergillus fumigatus.

Invasive Pulmonary Aspergillosis, which is caused by the filamentous fungus Aspergillus fumigatus, is a life-threatening infection for immunosuppressed patients. Chromatin structure regulation is important for genome stability maintenance and has the potential to drive genome rearrangements and affect virulence and pathogenesis of pathogens. Here, we performed the first A. fumigatus global chromatin profiling of two histone modifications, H3K4me3 and H3K9me3, focusing on the two most investigated A. fumigatus clinical isolates, Af293 and CEA17. In eukaryotes, H3K4me3 is associated with active transcription, while H3K9me3 often marks silent genes, DNA repeats, and transposons. We found that H3K4me3 deposition is similar between the two isolates, while H3K9me3 is more variable and does not always represent transcriptional silencing. Our work uncovered striking differences in the number, locations, and expression of transposable elements between Af293 and CEA17, and the differences are correlated with H3K9me3 modifications and higher genomic variations among strains of Af293 background. Moreover, we further showed that the Af293 strains from different laboratories actually differ in their genome contents and found a frequently lost region in chromosome VIII. For one such Af293 variant, we identified the chromosomal changes and demonstrated their impacts on its secondary metabolites production, growth and virulence. Overall, our findings not only emphasize the influence of genome heterogeneity on A. fumigatus fitness, but also caution about unnoticed chromosomal variations among common laboratory strains.

Mining novel cis-regulatory elements from the emergent host Rhodosporidium toruloides using transcriptomic data.

The demand for robust microbial cell factories that produce valuable biomaterials while resisting stresses imposed by current bioprocesses is rapidly growing. Rhodosporidium toruloides is an emerging host that presents desirable features for bioproduction, since it can grow in a wide range of substrates and tolerate a variety of toxic compounds. To explore R. toruloides suitability for application as a cell factory in biorefineries, we sought to understand the transcriptional responses of this yeast when growing under experimental settings that simulated those used in biofuels-related industries. Thus, we performed RNA sequencing of the oleaginous, carotenogenic yeast in different contexts. The first ones were stress-related: two conditions of high temperature (37 and 42°C) and two ethanol concentrations (2 and 4%), while the other used the inexpensive and abundant sugarcane juice as substrate. Differential expression and functional analysis were implemented using transcriptomic data to select differentially expressed genes and enriched pathways from each set-up. A reproducible bioinformatics workflow was developed for mining new regulatory elements. We then predicted, for the first time in this yeast, binding motifs for several transcription factors, including HAC1, ARG80, RPN4, ADR1, and DAL81. Most putative transcription factors uncovered here were involved in stress responses and found in the yeast genome. Our method for motif discovery provides a new realm of possibilities in studying gene regulatory networks, not only for the emerging host R. toruloides, but for other organisms of biotechnological importance.

Corrigendum to analysis of the phosphorylome of Trichoderma reesei cultivated on sugarcane bagasse suggests post-translational regulation of the secreted glycosyl hydrolase Cel7A.

[This corrects the article DOI: 10.1016/j.btre.2021.e00652.].

Nanopore Sequencing Provides Rapid and Reliable Insight Into Microbial Profiles of Intensive Care Units.

Fast and accurate identification of pathogens is an essential task in healthcare settings. Second-generation sequencing platforms such as Illumina have greatly expanded the capacity with which different organisms can be detected in hospital samples, and third-generation nanopore-driven sequencing devices such as Oxford Nanopore's minION have recently emerged as ideal sequencing platforms for routine healthcare surveillance due to their long-read capacity and high portability. Despite its great potential, protocols and analysis pipelines for nanopore sequencing are still being extensively validated. In this work, we assess the ability of nanopore sequencing to provide reliable community profiles based on 16S rRNA sequencing in comparison to traditional Illumina platforms using samples collected from Intensive Care Units of a hospital in Brazil. While our results demonstrate that lower throughputs may be a shortcoming of the method in more complex samples, we show that the use of single-use Flongle flowcells in nanopore sequencing runs can provide insightful information on the community composition in healthcare settings.

Analysis of the phosphorylome of trichoderma reesei cultivated on sugarcane bagasse suggests post-translational regulation of the secreted glycosyl hydrolase Cel7A.

Trichoderma reesei is one of the major producers of holocellulases. It is known that in T. reesei, protein production patterns can change in a carbon source-dependent manner. Here, we performed a phosphorylome analysis of T. reesei grown in the presence of sugarcane bagasse and glucose as carbon source. In presence of sugarcane bagasse, a total of 114 phosphorylated proteins were identified. Phosphoserine and phosphothreonine corresponded to 89.6% of the phosphosites and 10.4% were related to phosphotyrosine. Among the identified proteins, 65% were singly phosphorylated, 19% were doubly phosphorylated, 12% were triply phosphorylated, and 4% displayed even higher phosphorylation. Seventy-five kinases were predicted to phosphorylate the sites identified in this work, and the most frequently predicted serine/threonine kinase was PKC1. Among phosphorylated proteins, four glycosyl hydrolases were predicted to be secreted. Interestingly, Cel7A activity, the most secreted protein, was reduced to approximately 60% after in vitro dephosphorylation, suggesting that phosphorylation might alter Cel7A structure, substrate affinity, and targeting of the substrate to its carbohydrate-binding domain. These results suggest a novel post-translational regulation of Cel7A.

Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity.

Any descriptive language is necessarily metaphoric and interpretative. Two somewhat overlapping-but not identical-languages have been thoroughly employed in the last decade to address the issue of regulatory complexity in biological systems: the terminology of network theory and the jargon of electric circuitry. These approaches have found many formal equivalences between the layout of extant genetic circuits and the architecture of man-made counterparts. However, these languages still fail to describe accurately key features of biological objects, in particular the diversity of signal-transfer molecules and the diffusion that is inherent to any biochemical system. Furthermore, current formalisms associated with networks and circuits can hardly face the problem of multi-scale regulatory complexity-from single molecules to entire ecosystems. We argue that the language of economic theory might be instrumental not only to portray accurately many features of regulatory networks, but also to unveil aspects of the biological complexity problem that remain opaque to other types of analyses. The main perspective opened by the economic metaphor when applied to control of microbiological activities is a focus on metabolism, not gene selfishness, as the necessary background to make sense of regulatory phenomena. As an example, we analyse and reinterpret the widespread phenomenon of catabolite repression with the formal frame of the consumer's choice theory.

Combining Functional Genomics and Whole-Genome Sequencing to Detect Antibiotic Resistance Genes in Bacterial Strains Co-Occurring Simultaneously in a Brazilian Hospital.

(1) Background: The rise of multi-antibiotic resistant bacteria represents an emergent threat to human health. Here, we investigate antibiotic resistance mechanisms in bacteria of several species isolated from an intensive care unit in Brazil. (2) Methods: We used whole-genome analysis to identify antibiotic resistance genes (ARGs) and plasmids in 34 strains of Gram-negative and Gram-positive bacteria, providing the first genomic description of Morganella morganii and Ralstonia mannitolilytica clinical isolates from South America. (3) Results: We identified a high abundance of beta-lactamase genes in resistant organisms, including seven extended-spectrum beta-lactamases (OXA-1, OXA-10, CTX-M-1, KPC, TEM, HYDRO, BLP) shared between organisms from different species. Additionally, we identified several ARG-carrying plasmids indicating the potential for a fast transmission of resistance mechanism between bacterial strains. Furthermore, we uncovered two pairs of (near) identical plasmids exhibiting multi-drug resistance. Finally, since many highly resistant strains carry several different ARGs, we used functional genomics to investigate which of them were indeed functional. In this sense, for three bacterial strains (Escherichia coli, Klebsiella pneumoniae, and M. morganii), we identified six beta-lactamase genes out of 15 predicted in silico as those mainly responsible for the resistance mechanisms observed, corroborating the existence of redundant resistance mechanisms in these organisms. (4) Conclusions: Systematic studies similar to the one presented here should help to prevent outbreaks of novel multidrug-resistant bacteria in healthcare facilities.

Benchmarking Bacterial Promoter Prediction Tools: Potentialities and Limitations.

The promoter region is a key element required for the production of RNA in bacteria. While new high-throughput technology allows massively parallel mapping of promoter elements, we still mainly rely on bioinformatics tools to predict such elements in bacterial genomes. Additionally, despite many different prediction tools having become popular to identify bacterial promoters, no systematic comparison of such tools has been performed. Here, we performed a systematic comparison between several widely used promoter prediction tools (BPROM, bTSSfinder, BacPP, CNNProm, IBBP, Virtual Footprint, iPro70-FMWin, 70ProPred, iPromoter-2L, and MULTiPly) using well-defined sequence data sets and standardized metrics to determine how well those tools performed related to each other. For this, we used data sets of experimentally validated promoters from Escherichia coli and a control data set composed of randomly generated sequences with similar nucleotide distributions. We compared the performance of the tools using metrics such as specificity, sensitivity, accuracy, and Matthews correlation coefficient (MCC). We show that the widely used BPROM presented the worse performance among the compared tools, while four tools (CNNProm, iPro70-FMWin, 70ProPred, and iPromoter-2L) offered high predictive power. Of these tools, iPro70-FMWin exhibited the best results for most of the metrics used. We present here some potentials and limitations of available tools, and we hope that future work can build upon our effort to systematically characterize this useful class of bioinformatics tools.IMPORTANCE The correct mapping of promoter elements is a crucial step in microbial genomics. Also, when combining new DNA elements into synthetic sequences, predicting the potential generation of new promoter sequences is critical. Over the last years, many bioinformatics tools have been created to allow users to predict promoter elements in a sequence or genome of interest. Here, we assess the predictive power of some of the main prediction tools available using well-defined promoter data sets. Using Escherichia coli as a model organism, we demonstrated that while some tools are biased toward AT-rich sequences, others are very efficient in identifying real promoters with low false-negative rates. We hope the potentials and limitations presented here will help the microbiology community to choose promoter prediction tools among many available alternatives.

Unraveling the Complex Interplay of Fis and IHF Through Synthetic Promoter Engineering.

Bacterial promoters are usually formed by multiple cis-regulatory elements recognized by a plethora of transcriptional factors (TFs). From those, global regulators are key elements since these TFs are responsible for the regulation of hundreds of genes in the bacterial genome. For instance, Fis and IHF are global regulators that play a major role in gene expression control in Escherichia coli, and usually, multiple cis-regulatory elements for these proteins are present at target promoters. Here, we investigated the relationship between the architecture of the cis-regulatory elements for Fis and IHF in E. coli. For this, we analyze 42 synthetic promoter variants harboring consensus cis-elements for Fis and IHF at different distances from the core -35/-10 region and in various numbers and combinations. We first demonstrated that although Fis preferentially recognizes its consensus cis-element, it can also recognize, to some extent, the consensus-binding site for IHF, and the same was true for IHF, which was also able to recognize Fis binding sites. However, changing the arrangement of the cis-elements (i.e., the position or number of sites) can completely abolish the non-specific binding of both TFs. More remarkably, we demonstrated that combining cis-elements for both TFs could result in Fis and IHF repressed or activated promoters depending on the final architecture of the promoters in an unpredictable way. Taken together, the data presented here demonstrate how small changes in the architecture of bacterial promoters could result in drastic changes in the final regulatory logic of the system, with important implications for the understanding of natural complex promoters in bacteria and their engineering for novel applications.

CRZ1 regulator and calcium cooperatively modulate holocellulases gene expression in Trichoderma reesei QM6a.

Trichoderma reesei is the main filamentous fungus used in industry to produce cellulases. Here we investigated the role of CRZ1 and Ca2+signaling in the fungus T. reesei QM6a concerning holocellulases production. For this, we first searched for potential CRZ1 binding sites in promoter regions of key genes coding holocellulases, as well as transcriptional regulators and sugar and calcium transporters. Using a nearly constructed T. reeseiAcrz1 strain, we demonstrated that most of the genes expected to be regulated by CRZ1 were affected in the mutant strain induced with sugarcane bagasse (SCB) and cellulose. In particular, our data demonstrate that Ca2+ acts synergistically with CRZ1 to modulate gene expression, but also exerts CRZ1-independent regulatory role in gene expression in T. reesei, highlighting the role of the major regulator Ca2+ on the signaling for holocellulases transcriptional control in the most part of cellulases genes here investigated. This work presents new evidence on the regulatory role of CRZ1 and Ca2+ sensing in the regulation of cellulolytic enzymes in T. reesei, evidencing significant and previously unknown function of this Ca2+sensing system in the control key transcriptional regulators (XYR1 and CRE1) and on the expression of genes related to sugar and Ca2+ transport.

Turning the Screw: Engineering Extreme pH Resistance in Escherichia coli through Combinatorial Synthetic Operons.

Adoption of microorganisms as platforms for sustainable biobased production requires host cells to be able to withstand harsh conditions, usually very distant from those in which these organisms are naturally adapted to thrive. However, novel survival mechanisms unearthed by the study of microbiomes from extreme habitats may be exploited to enhance microbial robustness under the strict conditions needed for different industrial appplications. In this work, synthetic biology approaches were used to engineer enhanced acidic resistance in Escherichia coli through the characterization of a collection of unique operons composed of combinatorial assemblies of three novel genes from an extreme environment and three synthetic ribosome binding sites. The results here presented illustrate the efficacy of combining different metagenomic genes for resistance in synthetic operons, as expression of these gene clusters increased hundred-fold the survival percentage of cells exposed to an acidic shock in minimal media at pH 1.9 under aerobic conditions.

Functional Characterization of Clinical Isolates of the Opportunistic Fungal Pathogen Aspergillus nidulans.

Aspergillus nidulans is an opportunistic fungal pathogen in patients with immunodeficiency, and virulence of A. nidulans isolates has mainly been studied in the context of chronic granulomatous disease (CGD), with characterization of clinical isolates obtained from non-CGD patients remaining elusive. This study therefore carried out a detailed biological characterization of two A. nidulans clinical isolates (CIs), obtained from a patient with breast carcinoma and pneumonia and from a patient with cystic fibrosis that underwent lung transplantation, and compared them to the reference, nonclinical FGSC A4 strain. Both CIs presented increased growth in comparison to that of the reference strain in the presence of physiologically relevant carbon sources. Metabolomic analyses showed that the three strains are metabolically very different from each other in these carbon sources. Furthermore, the CIs were highly susceptible to cell wall-perturbing agents but not to other physiologically relevant stresses. Genome analyses identified several frameshift variants in genes encoding cell wall integrity (CWI) signaling components. Significant differences in CWI signaling were confirmed by Western blotting among the three strains. In vivo virulence studies using several different models revealed that strain MO80069 had significantly higher virulence in hosts with impaired neutrophil function than the other strains. In summary, this study presents detailed biological characterization of two A. nidulanssensu stricto clinical isolates. Just as in Aspergillus fumigatus, strain heterogeneity exists in A. nidulans clinical strains that can define virulence traits. Further studies are required to fully characterize A. nidulans strain-specific virulence traits and pathogenicity.IMPORTANCE Immunocompromised patients are susceptible to infections with opportunistic filamentous fungi from the genus Aspergillus Although A. fumigatus is the main etiological agent of Aspergillus species-related infections, other species, such as A. nidulans, are prevalent in a condition-specific manner. A. nidulans is a predominant infective agent in patients suffering from chronic granulomatous disease (CGD). A. nidulans isolates have mainly been studied in the context of CGD although infection with A. nidulans also occurs in non-CGD patients. This study carried out a detailed biological characterization of two non-CGD A. nidulans clinical isolates and compared the results to those with a reference strain. Phenotypic, metabolomic, and genomic analyses highlight fundamental differences in carbon source utilization, stress responses, and maintenance of cell wall integrity among the strains. One clinical strain had increased virulence in models with impaired neutrophil function. Just as in A. fumigatus, strain heterogeneity exists in A. nidulans clinical strains that can define virulence traits.