mLife

Bacteriophages exhibit staggering genomic diversity, yet phenotypic characterization remains bottlenecked by assays that generally yield simple binary infection outcomes. Quantitative traits can be extracted from standard liquid cultures, but many phages fail to produce a measurable effect in these settings, necessitating an improved quantitative approach on semi-solid media. While end-point plaque size is commonly used for quantitative phenotyping, it only provides a static snapshot that fails to disentangle phage performance across changing bacterial growth phases. Here, we report a cost-effective, scalable method to quantify the bacterial killing rates of diverse phages by continuously tracking plaque development using a consumer-grade flatbed scanner and custom open-source computer vision software. By fitting a minimal, five-parameter phenomenological model to the diverse BASEL collection of Escherichia coli phages, we accurately captured plaque expansion dynamics across all bacterial growth phases. We discovered that plaque expansion parameters vary significantly across individual phages and taxonomic families, with the initial expansion rate demonstrating an inverse correlation with viral genome size. Notably, these plaque expansion dynamics did not correlate with bacterial collapse times in liquid culture, indicating that they represent distinct physiological and kinetic processes. Finally, we demonstrated that phage taxonomic family can be predicted with 72.1% accuracy solely from dynamic plaque parameters using a random forest classifier, whereas conventional endpoint plaque sizes held no predictive power. This time-lapse phenotyping approach yields robust kinetic data superior to end-point measurements, establishing a scalable platform to accelerate phage discovery and genotype-phenotype mapping.

Environmental biosurveillance requires technologies that can rapidly translate genomic information into sensitive, specific, and deployable detection tools. Here, we present the first proof-of-concept application of SENTINEL (Smart Environmental Nucleic-acid Tracking using Inference from Neural-networks for Early-warning Localization), integrating artificial-intelligence-guided target discovery, isothermal amplification, CRISPR-based detection, and environmental DNA. Using Rattus exulans, R. norvegicus, and R. rattus as a challenging model of closely related invasive vertebrates, SENTINEL achieved species-specific discrimination without detectable cross-reactivity and sensitivities of 10 aM for plasmid DNA and 10 fg/uL for genomic DNA. Selected assays also showed reproducible semi-quantitative performance and successful eDNA validation. This first application of CRISPR-based environmental biosurveillance to terrestrial invasive vertebrates demonstrates a transferable framework for converting sequence data into field-deployable assays, with potential applications across biosecurity, conservation, agriculture, and environmental monitoring.

During phage infection, phages reprogram their host to secure energy, building blocks for their genome, and proteins, as well as the transcription and translation machinery required to replicate their genome and produce phage particles. This also includes reprogramming the host transcriptome to modify host gene expression and align it with the needs of the phage during infection. In this study, we present a putative early-expressed protein, Gp204 of Mosigvirus AV110, renamed Host transcription modulator (Htm), that has a broad impact on the extended-spectrum beta-lactamase (ESBL)- associated E. coli transcriptome. Overexpression of Htm decreased the growth rate of ESBL-E. coli and caused DNA condensation. Htm is structurally similar to bacterial enhancer-binding proteins, binds ssDNA, and alters the expression levels of over 700 E. coli genes when expressed. The majority of these genes were upregulated and are involved in multiple pathways, such as galactose metabolism and pentose and glucuronate interconversions, which may promote the synthesis of purines, pyrimidines, and histidine, all of which are required for the replication, transcription, and translation of the phage genome. Furthermore, energy production is likely to be upregulated by increased availability of acetyl-CoA, resulting from, among other factors, fatty acid degradation. Htm also downregulated the expression of the structural genes involved in flagellar assembly and multiple two-component systems. In conclusion, Htm could promote phage progeny production by increasing the availability of nucleotides, amino acids, and energy, while inhibiting non-essential processes, such as flagella formation.

The emerging neonatal bacterial pathogen Paenibacillus thiaminolyticus causes sepsis and postinfectious hydrocephalus, yet few tools are available to study or specifically detect it. We developed fluorescent probes based on cell wall-binding domains (CBDs) from bacteriophage-derived cell-wall hydrolases. CBDs can target structurally conserved cell-wall glycans, making them attractive affinity reagents for bacterial detection. None of the 11 probes we designed labeled intact cells under standard conditions, and we identified a surface layer (S-layer) that prevented access to their cell-wall targets. Brief acidic treatment (pH 2.0) released abundant high-molecular-mass proteins from the cell surface and enabled labeling by five distinct CBD probes, each containing a single C-terminal S-layer homology domain. Mass spectrometry identified the main released protein as an ortholog of the P. alvei SpaA S-layer protein, which we designate SlsA. Across 26 surveyed P. thiaminolyticus genomes, we identified four distinct SlsA variants with 58-79% pairwise amino acid identity, encoded within a conserved cluster homologous to the P. alvei S-layer-associated locus. After either the acidic treatment or a quick flame fixation, the lead probe mGL-BDPt9 labeled six P. thiaminolyticus strains, including three Ugandan clinical isolates, as well as a P. dendritiformis clinical isolate from a US infant, and three additional Paenibacillus species. mGL-BDPt9 also distinguished P. thiaminolyticus from five gram-positive sepsis pathogens in pairwise mixtures and detected it in a five-species suspension. Together, these findings establish the presence of an S-layer in P. thiaminolyticus and provide a rapid fluorescent labeling strategy for multiple Paenibacillus pathogens associated with neonatal paenibacilliosis.

To reduce health risks for human space exploration, it is important to model the effects of deep-space radiation on biological systems. The budding yeast Saccharomyces cerevisiae is a common model organism in space radiobiology, but little is known about how radiation damage to individual cells translates into the population-level effects that experiments measure. The Agent-Based Model for Microbial Populations Exposed to Radiation (AMMPER) is a spatially explicit agent-based model that simulates the effects of ionizing radiation on S. cerevisiae cells and produces population-level results. Its goal is to improve experiment design and data interpretation. AMMPER-1 demonstrated that agent-based simulations can qualitatively reproduce the effects of proton radiation on the growth rate of wild-type and DNA-repair mutant yeast. Here, we present AMMPER-2, which addresses limitations of the first model and expands its capabilities. AMMPER-2 incorporates the dynamics of the redox dye alamarBlue, a data type commonly generated in spaceflight microbiology experiments, and models the fate of reactive oxygen species in more detail. It also adds an improved graphical interface and runs faster. AMMPER-2 remains easy to execute in Python on a standard personal laptop, providing space biologists a tool to explore experimental conditions, generate hypotheses, and interpret data from microbial space radiation experiments.

Pyrogenic black carbon, such as biochar and wildfire char, possesses a reversible electron storage capacity (ESC), allowing it to serve as an electron donor or acceptor for microorganisms. However, not all the ESC is bioavailable, and the proportion available varies by culture. We hypothesized microbes capable of producing soluble redox mediators could access the ESC in biochar interior and thus utilize a higher percentage of the ESC than organisms without a mediator. To test this hypothesis, we grew wild-type Shewanella oneidensis (MR-1) and {Delta}bfe MR-1, a mutant incapable of exporting flavins, on air-oxidized wood biochar. Cell counts and redox titration show that both strains of MR-1 grew to similar cell densities and accessed comparable amounts of biochar ESC (~19%). The addition of exogenous flavin mononucleotide (FMN) yielded no measurable increase in final cell density, due to the strong adsorption of FMN to biochar. SEM images show attachment of MR-1 and {Delta}bfe to biochar surfaces and the presence of nanowires. This study demonstrates MR-1 can utilize biochar ESC for growth, through direct physical contact without the involvement of flavins. Understanding how microbes metabolize biochar provides insight into the impacts and biogeochemical roles of pyrogenic carbon.

CRISPR-Cas12a-based diagnostics are promising for field detection yet are often constrained by two step workflows or laborious optimization of one-pot reactions. Herein, we present a universal one-pot RPA/CRISPR-Cas12a strategy using base-mismatched crRNA in the seed region (positions 1-6). Introducing of single-nucleotide mismatches (complementary or error) into crRNA suppresses cis-cleavage activity, enabling amplification and detection in a single tube without PAM re-design or costly modified crRNA. Molecular dynamics (MD) simulations indicated that moderate potential energy (PE) variation correlates with improved screening performance, providing guidance for the selection of effective mismatches. This strategy successfully converted established two-step assays for Burkholderia pseudomallei and Brucella into one-pot formats, achieving detection of targets at 10 copies/L within 30 min with good specificity. Lyophilized pellets supported field testing on lithium-ion battery-powered devices, and experiments with simulated soil samples confirm the robustness of the approach. Together, this cost-effective, adaptable strategy can streamline RPA-CRISPR diagnostics for infectious disease surveillance.

Leveraging its non-invasive and convenient nature, urinary cytology offers a way for the early diagnosis and surveillance of bladder cancer (BCa). However, its sensitivity remains substantially inferior to the histopathological gold standard, rendering it inadequate to support precise clinical diagnosis. Here, we developed Lipid-featured Urine Cytology with Intelligent Detection (LUCID), an automated platform integrating single-cell stimulated Raman scattering (SRS) imaging and explainable AI model for accurate BCa detection. Following a newly developed standardized urine-preprocessing protocol, 19,678 urinary exfoliated cells from 26 BCa patients and 54 non-BCa participants were imaged using two-color SRS microscopy to simultaneously visualize lipid and protein distributions. For cell-level analysis, a customized deep-supervised Cellpose segmentation algorithm and a YOLO-based detection model were developed to enable precise single-cell segmentation and intracellular lipid droplet (LD) identification, respectively. Totally 20 quantitative morphological and compositional features per cell were extracted and 10 related principal components were obtained via principal component analysis. These features were subsequently integrated into a multiple-instance learning (MIL)-XGBoosting model, which achieved robust diagnostic performance, with 92.31% sensitivity, 87.04% specificity, and an AUC of 0.940. We found that morphological and compositional features provided complementary diagnostic information, while LD-associated features provided primary discriminative information, supporting intracellular LD phenotypes as potential urinary cellular markers of BCa. More importantly, LUCID demonstrated exceptional accuracy in detecting early-stage and small tumors, and showed higher concordance than FISH in a preliminary paired subset (5/6 vs. 2/6). Together, LUCID establishes a chemical imaging-AI paradigm that transforms molecular urine cytology.

Single-molecule enzyme activity analysis enables proteoform-resolved quantification of catalytically active enzyme molecules but remains limited by the need for enzyme-specific fluorogenic substrates, particularly for enzymes involved in central metabolism. Here we establish a biosensor-based framework that integrates genetically encoded fluorescent metabolite biosensors with single-molecule enzyme activity profiling (SEAP), enabling native enzymatic reactions to be monitored without enzyme-specific fluorogenic substrates. By employing different biosensors, the framework was applied to distinct metabolic enzymes, including lactate dehydrogenase and pyruvate kinase. Biosensor-based SEAP enables absolute quantification of active enzyme molecules, transforming conventional activity measurements (U/mL) into molecular abundance (molecules/mL). Applying this approach to single-cell functional proteoform analysis revealed functional heterogeneity in lactate dehydrogenase activity during T-cell activation. In addition, analysis of circulating enzymes showed that much of the plasma lactate dehydrogenase pool could be quantitatively accounted for by physiological erythrocyte turnover. These results establish genetically encoded fluorescent protein-based metabolite biosensors as modular optical transducers for single-molecule functional enzyme analysis across cellular and circulating samples.

Double-stranded RNA (dsRNA) biopesticides that silence essential fungal genes by spray-induced gene silencing (SIGS) are advancing towards registration, underpinned by the claim that silencing is confined to sequence-matched target organisms, a claim that has never been tested systematically against beneficial fungi. We screened twelve dsRNA constructs (the whole transcripts of eleven canonical antifungal target genes of Fusarium graminearum: three CYP51 sterol 14-demethylases, six chitin synthases and two {beta}-tubulins, plus a concatenation of the three CYP51 transcripts modelling the flagship CYP3RNA design) for perfect 21-nucleotide (21-nt) identity, the primary match criterion of recent regulatory bioinformatics frameworks, against the transcriptomes of seven non-target fungi, including commercial biocontrol agents (Trichoderma harzianum, T. virens, Beauveria bassiana, Metarhizium anisopliae, M. brunneum), the arbuscular mycorrhizal fungus Rhizophagus irregularis and Saccharomyces cerevisiae. Eleven of the twelve constructs carried off-target hazard; only CYP51C was completely clean. TUB2b carried 580 perfect 21-nt matches and contained no hazard-free window of 250 consecutive 21-mer start positions, whereas 26.6-60.0% of screened windows were hazard-free in the designable genes. Hits landed overwhelmingly on orthologues of the targeted gene and concentrated in the Hypocreales; R. irregularis and S. cerevisiae were nearly clean. These are hazard findings, not risk findings: perfect siRNA-length matches to beneficial fungi are common in canonical antifungal dsRNA targets, and current regulatory screening panels contain no fungus that would detect them.

HIV, tuberculosis (TB) and malaria are major infectious diseases that frequently co-circulate in high-burden settings, yet their diagnostic pathways remain largely disease-specific, complicating detection of coinfection and integration of antimicrobial-resistance testing. Here we developed HTM CARMEN, a highly multiplexed CRISPR-Cas13a molecular-testing framework that integrates detection of HIV-1, HIV-2, four Plasmodium species and Mycobacterium tuberculosis (Mtb) with detection of isoniazid-resistance-associated inhA and katG variants. We find that HTM CARMEN retained target selectivity and single-nucleotide discrimination across in vitro experiments and clinically relevant sample matrices, including whole blood, serum, sputum and dried blood spots. Across 20 contrived samples for each condition, the HTM panel supported detection of low-abundance targets across multiple matrices and frequently detected greater number of low-input samples as positive than qPCR under the conditions tested. In contrived P. falciparum/HIV-1 and Mtb/HIV-1 coinfections, both pathogens were simultaneously detected across a range of relative target concentrations. Using confirmed-positive clinical samples, HTM CARMEN achieved positive percent agreement of 95% for malaria (19/20), 95% for HIV-1 (19/20) and 100% for Mtb (30/30), with 100% negative percent agreement for each target in the confirmed-negative samples. Both HIV-1 and Mtb were additionally detected in all nine confirmed-positive HIV-TB coinfection specimens. These findings establish the feasibility of integrating pathogen detection, species differentiation, resistance-associated variant interrogation and coinfection detection within a programmable molecular-testing platform, providing a framework for high-throughput integrated testing in co-endemic settings.

The unprecedented rise in greenhouse gases such as carbon dioxide (CO2), and their detrimental effects on the atmosphere have intensified the current climate emergency. This challenge has inspired the development of novel carbon capture and utilisation (CCU) technologies, specifically microbial electrosynthesis (MES), for the bioelectrochemical fixation of CO2 into commodity chemical compounds (CCCs) using microbial biocatalysts. In this study, we systematically enriched and maintained robust mixed microbial communities of electroactive bacteria (EAB) from wastewater treatment sludge to serve as biocatalysts for MES. The MES performance of these biocatalysts under different operational conditions in bioelectrochemical systems (BES) reactors were investigated. BES reactors with enriched biocatalysts incubated and maintained under controlled conditions produced higher CCCs yields than those developed at ambient temperatures, while strict anaerobic operations further improved the MES efficiency compared to aerobic conditions. At an applied potential of -1000 mV vs Ag/AgCl, CO2 conversion reached up to 88.11%, significantly outperforming the lower potential (-600 mV) operation. These findings underscore the importance of systematic development of robust and stable biocatalysts, as well as maintaining strict anaerobic conditions, for achieving efficient MES performance, thereby demonstrating the potential of our approach for scalable industrial CCU applications.

Microalgae are promising biological platforms for sustainable energy production because they can convert photosynthetic activity into electricity in biophotovoltaic (BPV) systems. Identifying strains that grow well at elevated temperatures and under reduced irradiance is an important step toward BPV applications in seasonally variable environments. Microalgae were isolated from Seondong Lake (Seoul, Republic of Korea) during three sampling periods and screened for growth at different temperatures. Among 45 isolates, KHUC-11 showed the highest weighted growth index based on growth rates at 25, 30, and 35 under dark conditions and was selected for further characterization. Phylogenetic reconstruction of the 45S rRNA gene cluster, internal transcribed spacer 2 (ITS2) secondary-structure comparison, and morphological characterization supported the description of KHUC-11 as Desmodesmus ecotolerans sp. nov. D. ecotolerans produced more biomass than Chlamydomonas reinhardtii CC-125 under both moderate- and low-light conditions. Under low light, its final dry biomass concentrations were 1.81 and 1.58 times those of CC-125 under mixotrophic and photoautotrophic conditions, respectively. Despite its smaller cell size, D. ecotolerans showed higher photosynthetic performance than CC-125, and screen-printed electrode (SPE)-based electrochemical assays demonstrated enhanced anodic current generation and CV-derived estimated power output. Taken together, these findings identify D. ecotolerans as a novel climate-resilient Desmodesmus species with bioelectrochemical properties that support further evaluation for BPV applications.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a leading global health threat, especially due to multidrug-resistant strains. The first-line drug isoniazid (INH) and second-line drugs ethionamide (ETH) and prothionamide (PTH), are prodrugs that target the essential enoyl-ACP reductase InhA, a key enzyme involved in mycolic acid biosynthesis. Resistance to these drugs primarily arises from genetic mutations that inactivate the enzymatic function of their respective bioactivating enzymes. While the recent development of chemical boosters has shown promising results in restoring drug susceptibility in these mutants, genetic tools to dynamically modulate these pathways and fine-tune resistance could be extremely valuable to develop the next-generation of anti-InhA drugs. In this context, we leverage CRISPR interference (CRISPRi) to develop inducible and reversible genetic systems to fine-tune antibiotic susceptibility in Mycobacterium smegmatis (Msmeg) and Mtb. By using our CRISPRi-based approach we first generated an inducible inhA hypomorph, an essential target required for mycolic acid production and bacterial growth, therefore confirming that our method is fully functional. Then, we developed genetic tools to selectively target katG, ethA, and the transcriptional repressor ethR, in order to generate conditional hypomorphs with altered antibiotic susceptibility profiles. Antibiotic susceptibility assays confirmed that the conditional hypomorphs selectively displayed high-level resistance or hypervulnerability to INH or ETH upon CRISPRi induction. Altogether, this work establishes CRISPRi as a powerful genetic platform for selectively fine-tuning mycobacterial drug susceptibility, with potential applications in developing next-generation anti-TB therapies.

Bacteria utilize specialized protein secretion systems, particularly the type VI secretion system (T6SS), to dominate interbacterial competitions and kill the prey bacteria in a contact-dependent manner. Here, we report that disruption of the thioesterase gene encoded in the gramibactin (a bacterial siderophore) biosynthetic gene cluster activates the T6SS in the Burkholderia gladioli strain NGJ1, even in the absence of prey. The thioesterase mutant exhibits enhanced antibacterial activity, and ectopic expression of the gene complements the phenotype. Notably, thio mutant as well as thioesterase-complement strains show enhanced sensitivity to oxidative stress. The qRT-PCR analysis showed that the thio mutant had upregulated expression of RpoN1, a sigma factor, which positively regulates T6SS functions. We demonstrate that rpoN1 mutant is hypersensitive to oxidative stress and defective in T6SS activation, whereas overexpression of RpoN1 enhances oxidative stress tolerance in NGJ1 and activates T6SS. We propose that, during interbacterial conflicts encountered during plant colonization, NGJ1 faces an oxidative-stress-enriched environment that modulates thioesterase expression, induces RpoN1, and activates T6SS. Overall, our study exemplifies the broader physiological role of Thioesterase in maintaining intracellular ROS homeostasis in bacteria and stimulating antibacterial activity.

Microbial metabolism in extreme environments has long been shrouded in profound mystery. Distinct from the direct assimilation of exogenous organic carbon, microorganisms predominantly exploit inorganic nitrogen substrates as their primary nitrogen sources. As a critical nitrogen assimilation pathway, assimilatory nitrite reduction plays a vital role in microbial nitrogen acquisition, especially in extreme environments with poor nutrient availability. In this study, we identified the coexistence of two isoforms of NADH-dependent nitrite reductase in single genomes. NIR- and NIR-{beta}, named based on their phylogenetic relationships, exhibit significant differences in protein sequence, genomic context and evolutionary relationship, a phenomenon that has rarely been reported before. They also show distinct expression patterns, which may allow bacteria to achieve efficient nitrogen conversion in nutrient-poor environments. This finding calls for a revision of the conventional model of nitrogen cycling gene combinations inferred from common model microorganisms and holds numerous implications for how we understand and study the environmental adaptation mechanisms of extremophiles.

Genome-scale metabolic models guide rational strain engineering, while high-throughput insertional mutagenesis enables systematic investigation of gene function and condition-dependent growth requirements. However, a platform integrating high-density transposon insertion sequencing with matched stoichiometric, thermodynamic, enzyme-constrained and combined metabolic models has not, to our knowledge, been reported in Aspergillus. Here, we establish a unified experimental and computational platform in Aspergillus niger ATCC1015 by combining curated metabolic reconstruction, Ac/Ds transposon mutagenesis and machine-learning-based essentiality prediction. Our stoichiometric reconstruction, FBA1015, expands reaction coverage and compartmental resolution beyond four published reconstructions. From FBA1015, we developed three models sharing its biochemical network: GECKO light with enzyme-capacity constraints, TFA with thermodynamic constraints and ecTFA combining both. FBA1015 and its derivatives performed competitively in phenotype benchmarks, with added constraints improving agreement with measured growth and gas exchange. Insertion libraries grown in rich medium, glucose or xylose achieved genome-wide densities of one distinct site per 21--112bp across individual libraries. A machine-learning classifier integrated insertion patterns and gene structure to predict essentiality across 11,111 genes. Applying a stringent consensus criterion identified 709 genes predicted essential on both glucose and xylose as sole carbon sources, and 91 predicted essential on one but nonessential on the other. Integration with simulated gene deletions prioritizes candidate genes and testable pathway hypotheses, connecting high-throughput genetic screening with mechanistic analysis for fungal functional investigation and strain engineering.

To face the fungal infections that affect millions of people every year, a better understanding of resistance-conferring mutations is necessary. One obstacle is the considerable time and effort necessary to construct point mutations in pathogenic species, which is required to validate their role in resistance. To overcome this problem, we have constructed a series of heterologous expression plasmids that allow expression of the ERG3, ERG6 and ERG11 orthologs from Candida albicans, Candidozyma auris, Nakaseomyces glabratus, Candida parapsilosis, Candida tropicalis, and the CYP51A and CYP51B genes of Aspergillus fumigatus. All orthologs are codon-optimized and synthesized for expression in the budding yeast Saccharomyces cerevisiae. We also constructed host strains in which the native ERG genes are either deleted or under the control of a repressible promoter. We showed that all orthologs complement the function of the native genes when expressed from the S. cerevisiae native promoters. We recreated previously reported resistance-conferring mutations, and showed that expression in S. cerevisiae replicates known resistance phenotypes. We then used the system to test corresponding mutations across orthologs, and showed that most resistance phenotypes are conserved. The heterologous expression system allows rapid construction of mutants, by taking advantage of the abundant genetic tools available for S. cerevisiae. Screening of mutants can be done within a few experimental steps, greatly increasing the throughput compared to the introduction of mutations in pathogenic fungi. As a proof-of-concept, we used the system to discover new resistance-conferring mutations, using random mutagenesis. All plasmids are made publicly available.

Stemflow transports water and biological material from forest canopies to the stem base, yet fungal studies of this flux have largely relied on microscopy of conidia. We used ITS2 metabarcoding to characterize fungal environmental DNA (eDNA) in stemflow from 15 trees - five each of American beech (Fagus grandifolia), sugar maple (Acer saccharum), and tulip poplar (Liriodendron tulipifera) in a closed-canopy forest in northeastern Ohio, USA. Stemflow was sampled during eight rain events in the leafless season (November 2024-May 2025), along with simulated bark and gutter washes to assess near-collar and collector signals. Natural stemflow communities were compositionally distinct from both controls and occupied greater ordination space. Ascomycota dominated across all samples, with Parafenestella, Cytospora, Microcera, Nectria, and Aureobasidium among the most abundant resolved genera and 13 of the 25 most abundant taxa differed significantly among host species. Woody-plant-associated endophytes, canker fungi, and saprobes dominated ecological guild composition. Storm event explained 28.6% of taxonomic variation, host tree species 24.1%, and their interaction 10.0%. Guild composition was more strongly structured by host species (35.4%) than storm event (20.4%). Lichenized fungi and lichen-associated fungi were detected. Thus, leafless-season stemflow integrates broad bark-associated fungal eDNA, including recurrent lichen-derived material, beyond the morphologically distinctive conidia captured by traditional methods. Stemflow eDNA therefore offers a minimally invasive means of monitoring canopy fungal diversity and the strong host and temporal filtering of DNA exported from woody surfaces.

Protein-protein interactions (PPIs) influence critical biological processes in pathogenic microorganisms, such as the human fungal pathogen, Cryptococcus neoformans. Fungal thermotolerance and stress response pathways are key virulence determinants that directly impact pathogen adaptation and survival and the infection process. To establish a comprehensive baseline of PPIs in C. neoformans and explore these interactions to infer functional roles for uncharacterized proteins, we applied size exclusion chromatography coupled with mass spectrometry to the secreted and cellular proteomes of the fungi. As a result, 216 and 1699 unique proteins were identified across 24 secretome and proteome fractions, respectively. The predicted secretome networks included expected proteins associated with vesicles and virulence, indicating a role in extracellular defense. Whereas the cryptococcal proteome highlighted interactions among proteins with defined roles in fungal virulence for protein stability and thermotolerance, including two previously uncharacterized proteins, CNAG_00287 and CNAG_05199, putatively involved in complex formation with heat-shock proteins (HSP). Based on sequence and structure homology, we propose that CNAG_00287 is a tetratricopeptide repeat-containing co-chaperone that modulates Hsp 70 activity and CNAG_05199 functions as a Hsp70. We validated the thermotolerance role of CNAG_00287 in heat-related stress, as its absence significantly impaired fungal growth in nutrient-limited media at 37 {degrees}C. Together, this work resolves virulence-associated PPIs within C. neoformans and reveals new molecular regulators of thermotolerance that underpin fungal pathogenicity.