Arnaud Droit

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. Putative functions of the non-structural protein 2 (Nsp2) have been studied in vitro , but how Nsp2 affects pathogenesis and overall viral fitness in vivo has yet to be demonstrated. Here, we generated a recombinant SARS-CoV-2 lacking Nsp2 (ΔNsp2) to investigate its role in viral replication and disease. While ΔNsp2 replicated comparably to wild-type virus in vitro and in vivo , its deletion resulted in markedly attenuated disease in K18-hACE2 mice. Wild-type virus infection induced a strong pro-inflammatory response associated with increased recruitment of monocytes and macrophages, whereas ΔNsp2 virus 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. IMPORTANCE: The COVID-19 pandemic has officially ended, yet the virus that causes it continues to circulate and evolve, making it important to understand what makes it cause severe disease. This study focuses on a viral protein called Nsp2 and asks what role it plays in driving illness. Using an animal model, we observed that when Nsp2 was removed from the virus, infected animals became far less sick, even though the virus itself replicated just as well. Instead of triggering the harmful, excessive inflammation typically seen with the full virus, the modified virus prompted a more balanced immune response. Further investigation revealed that Nsp2 interacts directly with the host’s genetic machinery, suggesting a mechanism by which the virus hijacks immune signaling to worsen disease. These findings pinpoint Nsp2 as a driver of COVID-19 severity and a promising target for future therapies aimed at reducing inflammation rather than simply fighting the virus itself.

Soil physical structure is a critical determinant of agricultural landscape resilience, yet standard pedotransfer functions estimate soil hydraulic and structural properties using static abiotic variables, often overlooking the biological mechanisms that actively organize soil structure. This study evaluates the predictive power of multi‐kingdom microbiome data (prokaryotes, fungi and microeukaryotes) for three key soil functions: soil organic carbon (SOC) stock, mean weight diameter (MWD) and macroporosity. Using a dataset of 2251 agricultural soil samples from Quebec, Canada, we benchmarked four machine learning algorithms (HGBR, RFR, XGBoost, SVR) and four data aggregation strategies. The integration of microbiome data with texture and climate variables achieved high peak predictive accuracy ( range: 0.70–0.82). Methodologically, high‐resolution compositional approaches (ASV‐level centered log‐ratio) and kingdom‐balanced absolute abundances consistently outperformed taxonomic or functional aggregations. The loss of predictive power at the family level indicates that traits governing soil physical modification are phylogenetically shallow and strain‐specific. Interpretability analysis using Shapley Additive Explanations (SHAP) revealed a clear functional hierarchy in soil assembly. Specific prokaryotic and fungal features drove biochemical stabilization and physical scaffolding via the microbial carbon pump and structural enmeshment dynamics. In contrast, the architectural openness of macroporosity was fundamentally constrained by abiotic physical limits (e.g., texture). Within this physical framework, specific microbial taxa, including anaerobic bacteria and microeukaryotic amoebae, functioned not as active engineers, but as high‐sensitivity bio‐indicators of the resulting aeration and hydrological connectivity. These results define soil physical organization as a biologically mediated hierarchy rather than a passive geological byproduct. Consequently, we propose shifting from static pedotransfer functions to a dynamic biotransfer framework that leverages multi‐kingdom omic signatures to monitor soil physical resilience and crop adaptation potential.

We describe a family with 11 members affected by oral, facial and digital findings, as well as adult‐onset multiple long bone insufficiency fractures mimicking atypical femur fractures (AFF) on radiographs. The analysis of the pedigree of this family suggested an X‐linked dominant inheritance. We performed whole‐exome sequencing in this family. A rare novel variant, p.Ala642Asp (NM_003611, c.1925C>A, hg19) of the OFD1 gene was the only candidate on the X chromosome segregating with the phenotype within the family. The OFD1 gene targeted sequencing was carried out on 49 patients with AFF and 100 healthy controls. We detected another rare variant, p.Lys668Asn (NM_003611, c.2004G>C, hg19) of the OFD1 gene, in a woman with AFF. The structure of OFD1 protein has been predicted by AlphaFold 3 (AF3). According to the AF3 model of OFD1, the destabilization of the dimerization of OFD1 by the p.Ala642Asp variant and the breaking of ionic bond by the p.Lys668Asn variant could be pathogenic. This family demonstrates that such insufficiency fractures of long bones may occur in patients with oral‐facial‐digital syndrome and provides novel insight into the pathophysiology of these fractures and their association with dental and facial hypoplasia.

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.

Pregnancy involves complex maternal–fetal adaptations largely driven by placental function. Placental disorders affect up to 15% of pregnancies and can lead to serious complications such as preeclampsia, underscoring the need for early, accessible diagnostic tools. This study used LC-MS/MS proteomics to characterize longitudinal urinary proteome changes during pregnancy, evaluating urine as a non-invasive monitoring medium. Urine samples from 87 healthy pregnancies, collected at three gestational time points, revealed dynamic proteomic changes linked to immune regulation, coagulation, tissue remodeling, and metabolic processes. Several placental proteins were detected in urine with temporal variation consistent with gestational progression. As proof of concept, comparison with preeclamptic pregnancies identified distinct urinary proteomic signatures, with dysregulated proteins detectable as early as the first trimester. These findings support urinary proteomics as a scalable, non-invasive approach for pregnancy monitoring and early detection of obstetrical complications.

Germline deletions of UDP-glycosyltransferase (UGT) UGT2B17 and UGT2B28 genes are common in human populations, yet their association with aging remains unclear. In this exploratory study, we analyzed data from 12,934 participants, mostly Caucasian, in the Canadian Longitudinal Study on Aging (CLSA) to assess cross-sectional and prospective associations between UGT deficiencies and aging-related health outcomes, including mortality, multimorbidity, allostatic load, disease burden and polypharmacy. Sex-specific associations emerged from cross-sectional analyses: UGT2B17-deficient males were less likely to report arthritis (adjusted odds ratio (OR_adj) = 0.73, 95% confidence interval (0.58-0.92), P = 0.008), UGT2B17-deficient females were less likely to report neurological problems (OR_adj = 0.69 (0.51-0.91), P = 0.01), and UGT2B28-deficient females were less likely to report thyroid disorders (OR_adj = 0.50 (0.29-0.82), P = 0.009). Despite these associations, multimorbidity remained prevalent across all groups and no significant associations were observed with all-cause mortality or longevity. Physiological dysregulation, assessed by the allostatic load index, was more prevalent in males. Among males aged ≥ 65, UGT2B28-deficiency was associated with reduced high dysregulation compared to UGT-proficient males (40.0% vs. 53.0%, P = 0.05), whereas UGT2B17 deficiency was associated with a higher prevalence in males (60.4% vs. 53.0%, P = 0.03). In addition, UGT2B17-deficient males had a lower prevalence of polypharmacy than UGT-proficient males (9.3% vs. 13.9%; P = 0.02). Overall, these findings indicate sex-specific associations between UGT2B17 and UGT2B28 deletions and selected healthspan-related phenotypes, suggesting that UGT-related genetic variation may modulate physiological pathways relevant to aging. Given the exploratory nature of this study and its reliance on prevalence data, the results are intended to be hypothesis-generating and will require confirmation in longer-term longitudinal studies.

Traditional case-control metagenomic studies are constrained by their dependence on taxonomic and functional databases. Because annotation occurs before differential analysis, they are limited to known elements and keep function and taxonomy separate. Although binning strategies have emerged to reconstruct genomes and mitigate this issue, they still require an assembly step, preventing the use of all available sequencing data. Here, we introduce META-DIFF, a pipeline based on differentially abundant k-mers independently of any prior annotation. From those k-mers, it reconstructs longer sequences and provides biological context, as well as the best set of unitigs to discriminate between conditions. Across both taxonomy-centric and functionally-centric benchmarks, it showed robust performance and displayed great reproducibility. It also behaved more conservatively than did other univariate methodologies, i.e. it maintained a high precision at the expense of recall, particularly in conditions of low fold-change and limited sequencing depth. The efficacy of META-DIFF was further validated through its application to a real-world colorectal cancer dataset, which produced both confirmatory and novel results compared with those of previous publications. The pipeline is able to exploit all reads and identify differentially abundant elements, including unknown DNA, prior to annotation. With the guidelines provided, META-DIFF provides users with great exploratory power to unravel microbiome changes.

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 .

Background: Rapid 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. Methods: We 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. Results: The 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 model’s ability to maintain predictive accuracy under external validation. Conclusions: This 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.

Background/Objectives: CEU-938, an innovative antimicrotubule prodrug bioactivated by cytochrome P450 1A1 (CYP1A1), represents a promising targeted alternative for cancer cells overexpressing this enzyme. To optimize its clinical utility and minimize off-target effects in breast cancer (BC) patients, this study aims to identify predictive biomarkers of CEU-938 efficacy. Methods: The antiproliferative activity of CEU-938 was assessed across a panel of 39 human breast cancer and non-tumorigenic cell lines. Differential expression analyses were subsequently performed to distinguish CEU-938-responsive from non-responsive cell lines using a threshold of 1000 nM. Candidate biomarkers identified through this approach were then validated by RT-qPCR and Western blot analyses. Results: CEU-938 demonstrated marked and selective antiproliferative activity across molecular subtypes of human breast cancer, with efficacy observed in approximately 40% of triple-negative breast cancer (TNBC), 70% of estrogen receptor-positive (ER⁺), and 80% of human epidermal growth factor receptor 2-positive (HER2⁺) breast cancer cell lines, while sparing non-tumorigenic human breast cells (MCF 10A, MCF-12A, 184B5). Differential expression analysis identified five candidate biomarkers associated with CEU-938 responsiveness, namely, FOXA1 (log2-fold change (LFC) = 3.1), RAB25 (LFC = 3.8), RHOV (LFC = 2.9), PRKCH (LFC = 1.6), and HDAC9 (LFC = −1.7). Among these, FOXA1 and RAB25 robustly validated by RT-qPCR and Western blot analyses, showing strong inverse correlations with CEU-938 sensitivity (Spearman correlation coefficients of −0.82 and −0.61, respectively, at the protein level). The predictive value of FOXA1 and RAB25 was further confirmed by Western blot analyses in two independent breast cell line models, the non-responsive MCF-12A and the responsive MDA-kb2. Conclusions: Collectively, these findings identify FOXA1 and RAB25 as robust predictive biomarkers of response to CEU-938. Notably, FOXA1 and RAB25 are strongly implicated in breast cancer biology, and FOXA1 has been directly linked to the aryl hydrocarbon receptor (AHR), the main regulator of CYP1A1. These results position CEU-938 as a strong precision-therapy candidate that combines target selectivity, a favorable toxicity profile, and biomarker-enabled patient stratification, with potential clinical benefit in ER⁺ and HER2⁺ enriched tumors, as well as a subset of TNBC.