Biology subjects

Gotti, C.

Publications and source records attributed to Gotti, C..

6 recordsLinked to original sources

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↗

bioRxiv2025

How Varenicline Works: Identifying Critical Receptor and Ligand-based Interactions

Approved by the FDA in 2006, varenicline became the first nicotinic-based therapeutic for smoking cessation and has since been used by tens of millions of smokers worldwide. Varenicline works by targeting the 4{beta}2 nicotinic acetylcholine receptor (nAChR), the primary focus for nicotine addiction, where ligand recognition by the receptor triggers ion channel opening. While widely recognized that vareniclines development was rooted in the well-established pharmacology of cytisine, the two compounds display notably different profiles, not only at nAChRs, but also at key off-target sites such as the 5-HT3 serotonin receptor. Despite vareniclines widespread use and proven efficacy as a smoking cessation aid, our knowledge of the precise molecular mechanism underlying its action, particularly the specific receptor-ligand interactions that underpin its functional specificity, remains incomplete. Through a multidisciplinary approach that integrates complementary fields of research, this study reveals the critical receptor-ligand interactions that distinguish varenicline from related nAChR agonists, such as cytisine and nicotine. Our findings reveal previously unrecognized, critical hydrogen bonding interactions within the 4{beta}2 binding sites, specifically involving 4T139, 4T183, and {beta}2S133, that are uniquely and selectively engaged by varenicline. Of these, {beta}2S133 emerged as the pivotal determinant of vareniclines function, with substitution by valine significantly impairing the ligand efficacy. Furthermore, the design and synthesis of novel varenicline analogues shed new light into the functional importance of the ligands quinoxaline moiety, revealing that not just the presence but also the precise positioning of this hydrogen bond acceptor are critical for receptor activation by varenicline. Together, these findings uncover a previously uncharacterized interaction network essential for vareniclines function at 4{beta}2, offering a deeper and more comprehensive framework for understanding its distinct pharmacological profile while expanding our broader understanding of how ligand binding is translated into function in these receptors. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=174 SRC="FIGDIR/small/659675v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@a77e39org.highwire.dtl.DTLVardef@4fec37org.highwire.dtl.DTLVardef@11d26a2org.highwire.dtl.DTLVardef@d35640_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

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↗

bioRxiv2023

Full-field strain distribution in hierarchical electrospun nanofibrous Poly-L(lactic) acid and Collagen based scaffolds for tendon and ligament tissue regeneration: a multiscale study

Regeneration of injured tendons and ligaments (T/L) is a worldwide need. In this study electrospun hierarchical scaffolds made of a poly (L-lactic) acid and collagen blend were developed reproducing all the multiscale levels of aggregation of these tissues. Scanning electron microscopy, microCT and tensile mechanical tests were carried out, including a multiscale digital volume correlation analysis to measure the full-field strain distribution of electrospun structures. The principal tensile and compressive strains detected the pattern of strains caused by the nanofibers rearrangement, while the deviatoric strains revealed the related internal sliding of nanofibers and bundles. The results of this study confirmed the biomimicry of such electrospun hierarchical scaffolds, paving the way to further tissue engineering and clinical applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/543145v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1a67baorg.highwire.dtl.DTLVardef@190149aorg.highwire.dtl.DTLVardef@38968borg.highwire.dtl.DTLVardef@123bb49_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Extensive and accurate benchmarking of DIA acquisition methods and software tools using a complex proteomic standard

Over the past decade, the data-independent acquisition mode has gained popularity for broad coverage of complex proteomes by LC-MS/MS and quantification of low-abundance proteins. However, there is no consensus in the literature on the best data acquisition parameters and processing tools to use for this specific application. Here, we present the most comprehensive comparison of DIA workflows on Orbitrap instruments published so far in the field of proteomics. Using a standard human 48 proteins mixture (UPS1 - Sigma) at 8 different concentrations in an E. coli proteome background, we tested 36 workflows including 4 different DIA window acquisition schemes and 6 different software tools (DIA-NN, DIA-Umpire, OpenSWATH, ScaffoldDIA, Skyline and Spectronaut) with or without the use of a DDA spectral library. Based on the number of proteins identified, quantification linearity and reproducibility, as well as sensitivity and specificity in 28 pairwise comparisons of different UPS1 concentrations, we summarize the major considerations and propose guidelines for choosing the DIA workflow best suited for LC-MS/MS proteomic analyses. Our 96 DIA raw files and software outputs have been deposited on ProteomeXchange for testing or developing new DIA processing tools.

bioinformatics↗

bioRxiv2019

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning

The identification of microbial species in biological samples is essential to many applications in health, food safety and environment. MALDI-TOF MS technology has become a tool of choice for microbial identification but it has several drawbacks including: it requires a long step of bacterial culture prior to analysis (24h), it has a low specificity and is not quantitative. We have developed a new strategy for identifying bacterial species in biological samples using specific LC-MS/MS peptidic signatures. In the first training step, deep proteome coverage of bacteria of interest is obtained in Data Independent Acquisition (DIA) mode, followed by the use of machine learning to define the peptides the most susceptible to distinguish each bacterial species from the others. Then, in the second step, this peptidic signature is monitored in biological samples using targeted proteomics. This method, which allows the bacterial identification from clinical specimens in less than 4h, has been applied to fifteen species representing 84% of all Urinary Tract Infections (UTI). More than 31000 peptides in 200 samples have been quantified by DIA and analyzed by machine learning to determine an 82 peptides signature and build prediction models able to classify the fifteen bacterial species. This peptidic signature was validated for its use in routine conditions using Parallel Reaction Monitoring on a capillary flow chromatography coupled to a Thermo Scientific Q Exactive HF-X instrument. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the 1x105 CFU/mL threshold commonly used by clinical laboratories. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.

microbiology↗