Biology subjects

Droit, A.

Publications and source records attributed to Droit, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Mapping virulence-associated protein interaction networks reveals regulators of thermotolerance in Cryptococcus neoformans

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.

microbiology↗

bioRxiv2026

Interplay of NuA4/TIP60 and PRC2 Complex Activities in Fusion Driven Endometrial Stromal Sarcoma

Low-grade endometrial stromal sarcoma (LGESS) exhibits frequent chromosomal translocations that fuse various subunits of the NuA4/TIP60 co-activator complex to subunits of the Polycomb Repressive Complex 2 (PRC2) complex. LGESS fusion proteins, such as the commonly occurring JAZF1-SUZ12, have been shown to upregulate genes through mislocalization of NuA4/TIP60 activity to Polycomb target genes. In this study, we characterized an interesting recurrent fusion protein in LGESS that fused a NuA4/TIP60 component, MBTD1, to EZHIP. EZHIP is a recently described vertebrate protein that enzymatically inhibits methyltransferase activity of the PRC2 complex and is a potent oncogene. The MBTD1-EZHIP fusion protein forms a chimeric TIP60-PRC2.1 complex and drastically reduces H3K27me3 levels at Polycomb target genes, similar to EZHIP-overexpressing cancers. However, unlike EZHIP overexpression, MBTD1-EZHIP requires mislocalization of NuA4/TIP60 activity through the MBTD1 protein to upregulate oncogenes. Despite differences in the finer molecular mechanisms, MBTD1-EZHIP and the common JAZF1-SUZ12 fusion protein upregulated similar sets of genes in cell lines, showing a convergence of oncogenic gene expression. Surprisingly, unlike in cellular models, JAZF1-SUZ12 translocated patient samples showed not only upregulation of genes driven by increased H4K8ac and decreased H3K27me3 but also downregulation of specific genes due to accumulated H3K27me3, revealing an additional oncogenic mechanism in LGESS.

cancer biology↗

bioRxiv2026

An in-depth, updated benchmark for 16S amplicon sequencing

Amplicon-based techniques provide a rapid and cost-effective approach for profiling microbial communities. However, the observed microbial diversity is influenced by a wide range of factors, encompassing pre-analytical steps such as the choice of primers and target regions, as well as the bioinformatic pipeline, including the selection of tools, reference databases, and parameter settings. Several benchmarks are already available in the literature, but the updates to important tools and databases, namely LotuS3, the Ribosomal Database Project and GreenGenes2, prompted our investigation. In this study, we conducted a comprehensive benchmark of the main bioinformatic tools and databases. Using seven regions for three publicly available mock communities of increasing complexity, we tested 38 possible combinations of sequence resolution algorithms (DADA2 stand-alone, LotuS3 (DADA2/UPARSE)), taxonomic classifiers and search tools (Kraken2, DECIPHER, RDP, MMseqs2, Lambda, and Metaxa2), and databases (SILVA, GreenGenes2, RDP, RefSeq, and Metaxa2). The region V1-V3, coupled with DADA2+MMseqs2+SILVA, DADA2+Metaxa2, or LotuS3 (DADA2)+RDP yielded the highest-quality estimates of the true diversity according to the metrics. We also demonstrated that even certain dominant genera remain difficult to detect, and that the quantification of all genera can be substantially over- or under-estimated, even when using optimal combinations of tools and reference databases.

bioinformatics↗

bioRxiv2026

ZNF768 regulates expression of E2F1 protein to drive G0/G1 transition and cell cycle progression.

Accurate and tightly coordinated cell cycle progression and cell proliferation are critical for development, growth and homeostasis of an organism. Recently, Zinc finger protein 768 (ZNF768) was identified as a transcription factor driving cellular proliferation, in both a p53-dependent and independent manner. ZNF768 interacts with and represses p53 functions to limit cell cycle delay. Independently, ZNF768 promotes the transcription of key regulators of the cell cycle machinery, although the mechanisms through which this occurs remain unknown. Here, we report that ZNF768 protein levels are tightly regulated during the cell cycle, and its depletion leads to cell cycle exit and induction of quiescence. We found that ZNF768 modulates the cell cycle, at least in part, by controlling expression of the major pro-proliferative transcription factor E2F1 independently of p53 activation. Consequently, depletion of ZNF768, which also represses expression of the key mitotic transcription factor and E2F1 target FOXM1, leads to numerous mitotic errors. Supporting these findings, cancer genomics analyses reveal that ZNF768 expression levels are positively associated with E2F1 and FOXM1 expression levels in human tumors, suggesting that cancer cells might use ZNF768 to override cell cycle arrest, sustain proliferation, and promote cancer progression. Altogether, our results reveal that ZNF768 modulates cell cycle entry and proliferation, at least in part by regulating E2F1 expression.

cell biology↗

bioRxiv2026

PeptiDIA: A Machine Learning Framework for Enhanced Peptide Identification in Fast-Gradient Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) mass spectrometry has become increasingly prevalent in proteomics as advances in instrumentation, chromatography, and computational analysis have enabled robust proteome identification across complex biological samples. However, analytical depth achieved with fast chromatographic gradients remains lower than that obtained using long-gradients, reflecting a throughput-depth trade-off. Here, we present PeptiDIA, a machine learning framework that enhances peptide identification in fast-gradient DIA data by leveraging paired fast and long-gradient acquisitions from identical samples. PeptiDIA processes DIA-NN outputs generated at relaxed false discovery rate thresholds to obtain expanded candidate peptide pools and trains gradient-boosted decision tree models using long-gradient identifications as reference labels. The model integrates DIA-NN features with engineered peptide descriptors and applies isotonic regression to calibrate probabilities, enabling controlled peptide recovery relative to the long-gradient reference. Applied to human and murine datasets spanning six tissues acquired on an Orbitrap Exploris 480, PeptiDIA increased peptide identifications by 25-34% at 1% target reference-discordance rate (RDR) and increased the number of protein groups containing at least one rescued peptide by 15-17%. Overall, PeptiDIA improves the identification depth of fast-gradient DIA-NN workflows without altering acquisition strategies. The framework is available as a web application and command-line tool at https://github.com/Jordano700/PeptiDIA.

bioinformatics↗

bioRxiv2026

SARS-CoV-2 Nsp2 reprograms host immunity to drive pathogenic inflammation

Despite the end of the COVID-19 pandemic, SARS-CoV-2 continues to circulate endemically, highlighting the need to better understand the viral determinants of pathogenesis. Non-structural protein 2 (Nsp2) has been implicated in host-virus interactions, yet its function remains poorly defined in the context of infection. Here, we generated a recombinant SARS-CoV-2 lacking Nsp2 ({Delta}Nsp2) to investigate its role in viral replication and disease. While {Delta}Nsp2 replicated comparably to wild-type virus in vitro and in vivo, its deletion resulted in markedly attenuated disease in K18-hACE2 mice. Wild-type infection induced a strong pro-inflammatory response associated with increased recruitment of monocytes and macrophages, whereas {Delta}Nsp2 infection promoted a more balanced antiviral response characterized by enhanced lymphocyte and NK cell recruitment. This was accompanied by reduced pulmonary and systemic inflammation and distinct transcriptional programs, including downregulation of pathways related to RNA processing and translation. Mechanistically, CLIP-seq and proximity labeling suggest that Nsp2 interacts with host RNA and components of the translational machinery. Together, our findings identify Nsp2 as a key virulence factor that drives immunopathology by skewing host immune responses, highlighting its role as a regulator of host-pathogen interactions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/723222v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@540e40org.highwire.dtl.DTLVardef@73c663org.highwire.dtl.DTLVardef@e5c535org.highwire.dtl.DTLVardef@f62d7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

Pairing Data Independent Acquisition and High-Resolution Full Scan for Fast Urinary Tract Infection Diagnosis

BackgroundRapid and accurate identification of urinary tract infection (UTI) pathogens is critical for effective treatment and combating antimicrobial resistance. Conventional culture-based diagnostics are slow, and standard tandem mass spectrometry workflows are resource-intensive. MethodsWe present a proof-of-concept workflow that integrates high-resolution data-independent acquisition (DIA) MS/MS on the Thermo Scientific Orbitrap Astral with MS1-only spectra from the Orbitrap Exploris 480. DIA data establish a reference panel of pathogen-specific peptides, which are then identified in MS1 spectra from urine samples. Machine learning models trained on these matched MS1 features were used to classify eight common uropathogens and non-infected controls across synthetic inoculations, pure cultures, and clinical patient samples. Model development employed a one-vs-all Random Forest (Ranger) framework with nested cross-validation for feature selection and hyperparameter tuning, followed by evaluation on an independent held-out external patient cohort. ResultsThe approach accurately distinguished bacterial species in both controlled inoculated samples and clinical patient samples. Using repeated nested cross-validation, the model achieved a mean Matthews Correlation Coefficient (MCC) of 0.88, indicating robust classification performance across resampled training partitions. Performance generalized to an independent patient cohort, achieving an MCC of 0.822, confirming the models ability to maintain predictive accuracy under external validation. ConclusionsThis proof-of-concept demonstrates that pairing DIA-derived peptide panels with MS1-only data acquired on a cost-effective instrument suitable for routine analysis, enables rapid, culture-free identification of UTI pathogens. The method provides a scalable, high-throughput platform suitable for clinical applications and establishes a foundation for broader biomarker discovery and potential quantitative workflows. Key Points- DIA-derived peptide panels enable pathogen detection using MS1-only measurements. - Machine learning classifies eight common uropathogens covering 84% of infections. - MS1-only workflows enable culture-free pathogen identification of 300 samples/day.

bioinformatics↗

bioRxiv2026

Establishment of Stable Immortalized Human Choroidal Melanocytes for Ocular Research

PurposeThe short lifespan of primary normal choroidal melanocytes (NCMs) in vitro represents a major barrier to mechanistic, functional, and translational studies of choroid biology and uveal melanoma (UM). This study aimed to establish and characterize immortalized human NCM lines that retain melanocytic function, maintain a non-cancerous profile, and are amenable to gene editing. MethodsNCMs from four donors were immortalized by lentiviral transduction of Cyclin-dependent kinase 4 (CDK4R24C), Cyclin D1, and human Telomerase reverse transcriptase (hTERT), establishing NCM-K4DT lines. Their morphology, melanocytic marker expression, proliferation and functional properties (melanin synthesis, tyrosinase activity) were evaluated. Genomic stability was assessed by targeted mutation profiling, karyotyping, and copy number variation analysis. The tumorigenicity was tested in immunodeficient mice. Plasmid-based CRISPR/Cas9 editing was performed to determine their suitability for gene editing. ResultsNCM-K4DT lines retained dendritic-shaped morphology, pigmentation, and expression of PMEL, TYRP1, Melan-A, and SOX10. Cells exhibited enhanced proliferative capacity with preserved cell cycle regulation. Melanin production and tyrosinase activity were comparable to primary NCMs. Genomic profiling confirmed the absence of UM-associated driver mutations and chromosomal abnormalities. In vivo growth assays demonstrated no tumorigenic potential. Notably, NCM-K4DT cells were efficiently edited by CRISPR/Cas9. ConclusionsNCM-K4DT lines represent stable, non-cancerous, and genetically tractable models for studying choroidal melanocyte biology, modeling UM-associated mechanisms, and advancing therapeutic development in ocular research.

cell biology↗

bioRxiv2025

Parkinson's disease risk factors are expressed at brain barriers.

Parkinsons disease (PD) is characterized by the selective loss of dopaminergic neurons in the substantia nigra pars compacta, but whether its etiology is cell autonomous remains unclear. Increasing evidence implicates the blood-central nervous system (CNS) barriers in disease development, highlighting the importance of identifying genetic risk factors linked to cells forming the cerebrovasculature to advance this emerging area of research. The objective of this study is to identify PD genetic risk factors associated with blood-brain (BBB) and blood-cerebrospinal fluid (BCSFB) barriers, and to validate protein localization in human tissue and experimental models. To do so, we integrated genome-wide association studies and single nuclei RNA-sequencing datasets from the human postmortem substantia nigra (SN), midbrain, or cortical samples from control and PD donors. An in-depth bioinformatics analysis identified genes enriched in cell types that form the multicellular architecture of brain barriers, including CAVIN2, ANXA1, ANO2, and LRP1B. We further validated whether corresponding proteins were present in cell types associated with the blood-CNS barriers in human and mouse post-mortem tissues, as well as in iPSC-differentiated cells and choroid plexus organoids. Results showed that quantifying the proportion of endothelial cells expressing PD-related genes was under-evaluated at the transcript level compared to immunofluorescence analyses. In addition, we observed that CAVIN2 and ANXA1 proteins were more abundant at the vasculature of the substantia nigra vs. cortex, and CAVIN2 protein levels were reduced in PD vs. control human postmortem tissues. In contrast, the investigation of mouse postmortem samples demonstrated that the CAVIN2 protein is only present in a subset of mouse blood vessels, compared to nearly all vessels in human tissue. Similarly, mouse ANXA1 protein localizes to dopaminergic neurons of the substantia nigra and not at the vasculature, as seen in human tissue. The primary outcome of this study is the identification of PD-relevant risk genes specifically expressed at brain barriers and enriched in PD-relevant brain regions. The secondary outcome is the demonstration of poor transcript-protein correlation in - at least - a subset of PD risk factors, and a low interspecies conservation of protein localization for the selected candidates. In conclusion, the BBB and BCSFB may represent understudied contributors to PD, endothelial-specific proteins appear differentially regulated compared to transcripts, and experimental models require comprehensive validation to ensure relevance to the human condition.

neuroscience↗

bioRxiv2025

Unveiling the temporal impact: Exploring dynamic changes in the paediatric solid tumour immune microenvironment through time

The composition of the tumour immune microenvironment (TIME) influences tumour evolution and responsiveness to immunotherapy. While longitudinal changes in TIME have been well-characterized in adult cancers, its dynamics in childhood cancers remain poorly documented, limiting our ability to predict treatment responses and tailor immunotherapeutic strategies. This study aimed to evaluate the plasticity of TIME in paediatric solid tumours, investigate its longitudinal evolution, and identify time-dependent immune alterations. Transcriptomic data from longitudinal samples of 27 paediatric patients (<21 years old) with relapsed or refractory solid tumours were analysed, encompassing 70 timepoints: 16 diagnoses and 54 successive relapses. TIME plasticity was assessed using gene expression clustering and immune cell infiltration enumeration. Patient-adjusted longitudinal analyses were performed using generalised linear mixed models (glmmSeq), adjusted for age and sex. Temporal associations of immune changes were further explored using dynamic regression models. Thirteen patients exhibited significant changes in their TIME profile, indicating high TIME plasticity. Over time, the TIME shifted toward a tolerogenic and immunosuppressive state, characterised by decreased activity in immune pathways (e.g., T cell receptor signalling) and enrichment of tolerogenic (e.g., macrophage differentiation) and oncogenic pathways (e.g., IL6-JAK-STAT3). The core enrichment of upregulated pathways contained key immunosuppressive factors: immune checkpoints (CTLA-4), tumour-associated macrophage activators (CSF1/CSF1R), T-regulatory cell activators (TGFB1), and immunosuppressive genes (IL10RA). This study provides evidence that the TIME in paediatric solid tumours is plastic and remodels towards immune depletion and tolerogenicity. This evolution may underlie treatment resistance and disease progression, underscoring the need for TIME-informed therapeutic approaches in paediatric oncology. Significance StatementThis article demonstrates the plasticity of the tumour immune micro-environment (TIME) of paediatric solid tumours throughout disease evolution. Longitudinal transcriptomic analyses of 70 tumour samples from 27 patients showed a progressive remodelling towards tolerogenicity and immune depletion. Key immunosuppressive factors, including immune checkpoints and tumour-associated macrophages, were identified as potential contributors to immune escape. These findings support the relevance of longitudinal immune monitoring in paediatric oncology and may inform future strategies for immunotherapeutic interventions.

cancer biology↗

bioRxiv2025

Concomitant acetylation and loading of H2A.Z by NuA4/TIP60 regulate target gene transcription

The human NuA4/TIP60 complex is a multi-subunit, dual enzymatic epigenetic factor and gene regulator. It bears histone acetyltransferase (HAT) activity towards the canonical histones H2A and H4 and the histone variant H2A.Z, a function that has been linked to gene activation. It also acts as a chromatin remodeling enzyme through ATP-dependent exchange of nucleosomal H2A-H2B dimers with H2A.Z-H2B, leading to incorporation of H2A.Z into chromatin at gene regulatory elements. NuA4/TIP60 is unique in merging two enzymatic activities targeting H2A.Z. Both NuA4/TIP60-dependent H2A.Z acetylation and remodeling have been linked to several physiological functions and pathologies, but studies have only focused on either one or the other enzymatic activity, and insights on functional coordination between them are lacking. Here, we leverage our EP400 rapid depletion system to explore and untangle the intricate links between H2A.Z acetylation by Tip60 (the HAT subunit) and loading on chromatin by EP400 (the remodeling subunit) through functional genomic and biochemical analyses. Our data support a mechanism in which H2A.Z is first pre-acetylated to allow for H2A.Zac-H2B dimer association with the complex before incorporation into chromatin, particularly at gene promoters to positively regulate transcription. As both H2A.Z-targeted enzymatic functions of NuA4/TIP60 have been linked to disease, albeit separately, our findings hold important implications for therapeutic intervention, where combinatorial targeting is a promising avenue.

molecular biology↗

bioRxiv2024

scMusketeers: Addressing imbalanced cell type annotation and batch effect reduction with a modular autoencoder

The growing number of single-cell gene expression atlases available offers a conceptual framework for improving our understanding of physio-pathological processes. To take full advantage of this revolution, data integration and cell annotation strategies need to be improved, in particular to better detect rare cell types and by better controlling batch effects in experiments. scMusketeers is a deep learning model that optimises the representation of latent data and solves both challenges. scMusketeers features three modules: (1) an autoencoder for noise and dimensionality reductions; (2) a focal loss classifier to enhance rare cell type predictions; and (3) an adversarial domain adaptation (DANN) module for batch effect correction. Benchmarking against state-of-the-art tools, including the UCE foundation model, showed that scMusketeers performs on par or better, particularly in identifying rare cell types. It also allows to transfer cell labels from single-cell RNA sequencing to spatial transcriptomics. With its modular and adaptable design, scMusketeers offers a versatile framework that can be generalized to other large-scale biological projects requiring deep learning approaches, establishing itself as a valuable tool for single-cell data integration and analysis.

bioinformatics↗

bioRxiv2024

Functional interaction between transcription factor Sfp1 and the NuA4 complex in response to nutrient availability

Ribosome biogenesis is a crucial process requiring enormous transcriptional output. In budding yeast, the expression of 138 ribosomal protein (RP) genes and over 200 ribosome biogenesis (RiBi) genes is regulated by an intricate network of factors, including the nutrient-sensitive transcription activator Sfp1 and the NuA4 coactivator/acetyltransferase complex. Nutrient starvation or inhibition of TORC1 by rapamycin leads to repression of RP and RiBi genes, in part through blocking Sfp1 nuclear localization and NuA4-dependent chromatin acetylation. Here, we demonstrate that Sfp1 physically interacts with NuA4 in a TORC1-dependent manner. Our results indicate that Sfp1, along with NuA4, regulate the transcription of RiBi and RP genes via distinct mechanisms depending on promoter architectures. Sfp1 promotes histone acetylation at the promoters without affecting NuA4 recruitment. In contrast, NuA4 does impact Sfp1 binding but specifically at two classes of RP genes. Importantly, NuA4 acetylates Sfp1 at lysines 655 and 657, regulating its function. Cells expressing Sfp1 with acetyl-mimicking mutations exhibit increased expression of RiBi genes while RP genes remain stable. However, the same mutants lead to the loss of Sfp1 binding/activity at RiBi genes when cells are under non-optimal growth conditions. Mimicking constitutive acetylation of Sfp1 also limits the transcriptional burst of RP genes upon addition of glucose. Altogether, these results draw an intricate functional relationship between Sfp1 and NuA4 to control ribosome biogenesis, fine-tuning transcription output in different growth conditions.

molecular biology↗

bioRxiv2024

LC-SRM combined with machine learning enables fast identification and quantification of bacterial pathogens in urinary tract infections

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate antibiotherapy to patients and to avoid the emergence of drug-resistant pathogens. While the gold standard requires 24h to 48h of bacteria culture prior MALDI-TOF species identification, we propose a culture-free workflow, enabling a bacterial identification and quantification in less than 4 hours using 1mL of urine. After a rapid and automatable sample preparation, a signature of 82 bacterial peptides, defined by machine learning, was monitored in LC-MS, to distinguish the 15 species causing 84% of the UTIs. The combination of the sensitivity of the SRM mode on a triple quadrupole TSQ Altis instrument and the robustness of capillary flow enabled us to analyze up to 75 samples per day, with 99.2% accuracy on bacterial inoculations of healthy urines. We have also shown our method can be used to quantify the spread of the infection, from 8x104 to 3x107 CFU/mL. Finally, the workflow was validated on 45 inoculated urines and on 84 UTI-positive urine from patients, with respectively 93.3% and 87.1% of agreement with the culture-MALDI procedure at a level above 1x105 CFU/mL corresponding to an infection requiring antibiotherapy. HIGHLIGHTS- LC-MS-SRM and machine learning to identify and quantify bacterial species of UTI - Fast sample preparation without bacterial culture and high-throughput MS analysis - Accurate quantification through calibration curves for 15 species of UTIs - Validation on inoculations (93% accuracy) and on patients specimens (87% accuracy)

microbiology↗

bioRxiv2024

Comprehensive benchmark of integrative strategies for analyzing microbiome-metabolome relationships

BackgroundThe exponential growth of high-throughput sequencing technologies was an incredible opportunity for researchers to combine various -omics within computational frameworks. Among these, metagenomics and metabolomics data have gained an increasing interest due to their involvement in many complex diseases. However, currently, no standard seems to emerge for jointly integrating both microbiome and metabolome datasets within statistical models. ResultsThus, in this paper we comprehensively benchmarked nineteen different integrative methods to untangle the complex relationships between microorganisms and metabolites. Methods evaluated in this paper cover most of the researchers goals such as global associations, data summarization, individual associations, and feature selection. Through an extensive and realistic simulation we identified best methods across questions commonly encountered by researchers. We applied the most promising methods in an application to real gut microbial datasets, unraveling complementary biological processes involved between the two omics. We also provided practical guidelines for practitioners tailored to specific scientific questions and data types. ConclusionIn summary, our work paves the way toward establishing research standards when mutually analyzing metagenomics and metabolomics data, building foundations for future methodological developments.

bioinformatics↗

bioRxiv2023

Concomitant effects of orchiectomy and intermittent hypoxia onhepatic oxidative stress, expression of flavin-containingmonooxygenases and transcriptomic profile in mice.

Intermittent hypoxia induces oxidative stress and alters hepatic metabolism, likely underlying the association of sleep apnea with non-alcoholic fatty liver disease. In male patients with sleep apnea, metabolic or liver diseases, the levels of testosterone are reduced, and in patients with metabolic diseases, low levels of testosterone are associated with oxidative stress. To assess potential interactions between testosterone and IH on hepatic oxidative stress we used sham-operated or orchiectomized (ORX) mice exposed to normoxia (Nx) or IH (6% O2, 12 cycles/h, 12h/day) for 2 weeks. The activity of prooxidant (NADPH oxidase - NOX), antioxidant enzymes (superoxide dismutase, catalase, and glutathione peroxidase - SOD, Cat, GPx), lipid peroxidation (MDA concentration) and the total concentration of glutathione (GSH) were measured in liver. IH induced a prooxidant profile of enzyme activity (lower SOD activity and higher NOX/SOD and NOX/Cat activity ratio) without altering hepatic MDA and GSH content. Using RNA sequencing followed by a pathway enrichment analysis we identified putative hepatic genes underlying the interactions between IH and testosterone. ORX and IH altered the expression of genes involved in oxidoreductase activities, cytochromes dependent pathways, and glutathione metabolism. Among the genes upregulated in ORX-IH mice, the flavin-containing monooxygenases (FMO) are particularly relevant since these are potent hepatic antioxidant that could help prevent overt oxidative stress in ORX-IH mice. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/541054v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@e50d42org.highwire.dtl.DTLVardef@1c996acorg.highwire.dtl.DTLVardef@1c33197org.highwire.dtl.DTLVardef@1eccd1a_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2023

Resolving the temporal splenic proteome during fungal infection for discovery of putative dual perspective biomarker signatures

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

microbiology↗

bioRxiv2022

BioDiscViz : a visualization support and consensus signature selector for BioDiscML results

Machine learning (ML) algorithms are powerful tools to find complex patterns and biomarker signatures where conventional statistical methods may fail to identify them. While the ML field made significant progress, state of the art methodologies to build efficient and non-overfitting models are not always applied in the litterature. To this purpose, automatic programs, such as BioDiscML, have been designed to identify biomarker signatures and correlated features while escaping overfitting using multiple evaluation strategies, such as cross validation, bootstrapping and repeated holdout. To further improve BioDiscML and reach a broader audience, better visualization support and flexibility in choosing the best models and signatures are needed. Thus, to provide researchers with an easily accessible and usable tool for in depth investigation of the results from BioDiscML outputs, we developed a visual interaction tool called BioDiscViz. This tool provides summaries, tables and graphics, in the form of Principal Component Analysis (PCA) plots, heatmaps and boxplots for the best model and the correlated features. Furthermore, this tool also provides visual support to extract a consensus signature from BioDiscML models using a combination of filters. BioDiscViz will be a great visual support for research implying machine learning, hence new opportunities in this field by opening it to a broader community.

bioinformatics↗