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Marchetti, L.

Publications and source records attributed to Marchetti, L..

13 recordsLinked to original sources

Selective targeting of the oligodendroglial GPR17 receptor improves myelin integrity and motor function in female SOD1G93A mice

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with no definitive disease-modifying therapies available, underscoring the urgent need to identify novel druggable targets. The G protein-coupled receptor GPR17 is a critical regulator of oligodendrocyte maturation and has emerged as a candidate target in ALS, yet its relevance to human disease and its therapeutic potential remain unclear. Here, we demonstrate that pathological GPR17 upregulation defines a conserved, pathologically immature oligodendroglial state in human ALS that can be pharmacologically leveraged to restore myelin integrity and improve functional outcome in vivo. Publicly available transcriptomics datasets and histological analyses revealed an increased abundance of GPR17-expressing immature oligodendrocytes in post-mortem human spinal cord tissue from ALS cases. Moreover, sustained activation of GPR17 with a selective agonist induced GPR17 internalization in heterologous expression systems and promoted the differentiation of primary oligodendrocyte precursors derived from SOD1G93A mice. Translating these findings in vivo, chronic treatment with a brain-penetrant GPR17 agonist derived from the same pharmacological class significantly extended survival, delayed body weight loss, and improved motor performance in female SOD1G93A mice, whereas male mice showed no therapeutic benefit. These effects were associated with restored oligodendrocyte maturation, preserved myelin integrity, motor neuron survival, and attenuated reactive gliosis in the spinal cord of female SOD1G93A mice, while milder effects were observed in males. Together, these findings establish oligodendroglial GPR17 as a conserved and pharmacologically actionable target in ALS and show that sustained in vivo GPR17 agonism can reprogram altered oligodendroglial states and slow disease progression in a sex-dependent manner.

pharmacology and toxicology↗

Physiologically Based Pharmacokinetic Modeling of mRNA-Encoded Therapeutics: A Multiscale Framework for LNP and Antibody Trafficking in Mice

Antibody-based therapeutics have revolutionized disease treatment, and recent advances in messenger RNA (mRNA) technologies have opened new opportunities for their intracellular production. In particular, in vitro-transcribed mRNA encapsulated in lipid nanoparticles (LNPs) enables targeted delivery to specific cells, where it can enable the synthesis of therapeutic antibodies with prolonged half-lives in a cost-effective manner. Despite rapidly growing experimental data, a modeling framework that integrates mRNA delivery, intracellular expression kinetics, and whole-body antibody disposition remains unavailable. To address this gap, we extended a Physiologically Based Pharmacokinetic model with a novel multiscale layer describing mRNA trafficking, cellular uptake, translation, and degradation. The integrated model was calibrated and validated using five datasets of mRNA-based cancer therapeutics, demonstrating strong predictive performance for the biodistribution of mRNA-encoded antibodies. The newly introduced mRNA layer, while minimally parameterized, effectively represents complex intracellular and systemic processes, enabling quantitative investigation of antibody biodistribution, optimization of dose scheduling, and providing an initial framework for future exploration of how LNP-mRNA formulation influences delivery and pharmacokinetics.

pharmacology and toxicology↗

A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations

Dynamical systems play a central role across the quantitative sciences, offering a powerful mathematical framework to describe, analyze, and predict the evolution of complex processes over time. In systems biology, dynamical systems provide a foundation for modeling and predicting the intricate behaviors of biological systems. Recent advances in data-driven approaches, such as Neural Ordinary Differential Equations (NODEs) and Universal Differential Equations (UDEs), have enabled the development of models that are either fully or partially data-driven. Integrating data-driven components into dynamical systems amplifies the challenge of generalization beyond training data, highlighting the need for robust methods to quantify uncertainty in out-of-distribution (OOD) scenarios--i.e., conditions not encountered during training. In this work, we investigate the reliability of uncertainty quantification (UQ) based on ensembles of models in the reconstruction of dynamical systems. We show that standard ensembles (i.e., models trained independently with different random initializations) risk producing overconfident predictions in previously unseen scenarios, as the models in the ensemble tend to exhibit similar behaviors. To address this issue, we propose a novel ensemble construction method for NODEs and UDEs that fosters diversity in the reconstructed vector field across models within specific regions of the state space, while maintaining explicit control over the fit on the training set. We first evaluate our method on numerical test cases derived from three models commonly used as benchmarks for data-driven reconstruction of dynamical systems: the Lotka-Volterra model, the damped oscillator, and the Lorenz system. We then apply the method to a biologically motivated model of cell apoptosis, considering more realistic conditions such as partial observability of the system outputs and noise in the training dataset. Overall, our results show that the proposed method improves the reliability of UQ in previously unseen scenarios compared with standard ensembles, especially where the latter exhibit overconfidence.

systems biology↗

B-cell precursor acute lymphoblastic leukaemia with IGH::CEBP rearrangement: what have we learnt over the years?

B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) is a haematologic malignancy marked by the rapid proliferation of immature B cells in the bone marrow. While BCP-ALL most commonly affects children aged 1-5 years, it remains the most prevalent subtype of ALL in adolescence and adulthood. Chromosomal translocations involving the immunoglobulin (IG) locus and partner genes are proven useful for risk stratification and guiding clinical trials for therapeutic decision. This includes translocations with CCAAT/enhancer-binding proteins (CEBP), which are particularly rare. This rarity has limited efforts to characterise their genetic and clinical profiles, making risk stratification for IGH::CEBP-rearranged BCP-ALL challenging. In this letter, we review the clinical and demographic characteristics of all reported IGH::CEBP cases prior to 2024 and introduce new cases, with preliminary analysis to encourage further investigation into this poorly understood subtype. This study delivers new insights into the molecular and cytogenetic landscape of IGH::CEBP rearrangements in BCP-ALL, and lays a foundation for further investigation into CEBP family roles in haematopoietic development and leukemogenesis, especially in the context of Down syndrome. Finally, it introduces the ongoing international collaborative effort to assemble the largest known IGH::CEBP cohort for comprehensive risk stratification and prognostic evaluation.

cancer biology↗

stormTB: A web-based simulator of a murine minimal-PBPK model for anti-tuberculosis treatments.

IntroductionTuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven and physiologically-based solutions for novel and historical compounds. However, this diverse modeling panorama may lack consistency, limiting result comparability. Drug-specific models are often tied to commercial software and developed on various platforms and languages, potentially hindering access and complicating the comparison of different compounds. MethodsThis work introduces stormTB: SimulaTOr of a muRine Minimal-pbpk model for anti-TB drugs. It is a web-based interface for our minimal physiologically based pharmacokinetic (mPBPK) platform, designed to simulate custom treatment scenarios for tuberculosis in murine models. The app facilitates visual comparisons of pharmacokinetic profiles, aiding in assessing drug-dose combinations. ResultsThe mPBPK model, supporting 11 anti-TB drugs, offers a unified perspective, overcoming the potential inconsistencies arising from diverse modeling efforts. The app, publicly accessible, provides a user-friendly environment for researchers to conduct what-if analyses and contribute to collective TB eradication efforts. The tool generates comprehensive visualizations of drug concentration profiles and pharmacokinetic/pharmacodynamic indices for TB-relevant tissues, empowering researchers in the quest for more effective TB treatments. stormTB is freely available at the link: https://apps.cosbi.eu/stormTB.

pharmacology and toxicology↗

Robust parameter estimation and identifiability analysis with Hybrid Neural Ordinary Differential Equations in Computational Biology

Parameter estimation is one of the central problems in computational modeling of biological systems. Typically, scientists must fully specify the mathematical structure of the model, often expressed as a system of ordinary differential equations, to estimate the parameters. This process poses significant challenges due to the necessity for a detailed understanding of the underlying biological mechanisms. In this paper, we present an approach for estimating model parameters and assessing their identifiability in situations where only partial knowledge of the system structure is available. The partially known model is extended into a system of Hybrid Neural Ordinary Differential Equations, which captures the unknown portions of the system using neural networks. Integrating neural networks into the model structure introduces two primary challenges for parameter estimation: the need to globally explore the search space while employing gradient-based optimization, and the assessment of parameter identifiability, which may be hindered by the expressive nature of neural networks. To overcome the first issue, we treat biological parameters as hyperparameters in the extended model, exploring the parameter search space during hyperparameter tuning. The second issue is then addressed by an a posteriori analysis of parameter identifiability, computed by introducing a variant of a well-established approach for mechanistic models. These two components are integrated into an end-to-end pipeline that is thoroughly described in the paper. We assess the effectiveness of the proposed workflow on test cases derived from three different benchmark models. These test cases have been designed to mimic real-world conditions, including the presence of noise in the training data and various levels of data availability for the system variables. Author summaryParameter estimation is a central challenge in modeling biological systems. Typically, scientists calibrate the parameters by aligning model predictions with measured data once the model structure is defined. Our paper introduces a workflow that leverages the integration between mechanistic modeling and machine learning to estimate model parameters when the model structure is not fully known. We focus mainly on analyzing the identifiability of the model parameters, which measures how confident we can be in the parameter estimates given the available experimental data and partial mechanistic understanding of the system. We assessed the effectiveness of our approach in various in silico scenarios. Our workflow represents a first step to adapting traditional methods used in fully mechanistic models to the scenario of hybrid modeling.

systems biology↗

Exploring alternative quorum sensing model structures and quorum quenching strategies

Bacterial quorum sensing (QS) is a cell-to-cell communication mechanism through which bacteria share information about cell density, and tune gene expression accordingly. Pathogens exploit QS to orchestrate virulence and regulate the expression of genes related to antimicrobial resistance. Despite the vast literature on QS, the properties of the underlying molecular network are not entirely clear. We compare two synthetic QS circuit architectures: in the first, a single positive feedback loop autoinduces the synthesis of the signal molecule; the second includes an additional positive feedback loop enhancing the synthesis of the signal molecule receptor. Our comprehensive analysis of the two systems and their equilibria highlights the differences in the bistable and hysteretic behaviors of the alternative QS structures. Finally, we investigate three different QS inhibition approaches; numerical analysis predicts their effect on the steady-state behavior of the two different QS models, revealing critical parameter thresholds that guarantee an effective QS suppression.

synthetic biology↗

Genomic epidemiology of third-generation cephalosporin-resistant Escherichia coli from Argentinian pig and dairy farms reveals animal-specific patterns of co-resistance and resistance mechanisms

Control measures are being introduced globally to reduce the prevalence of antibiotic resistant (ABR) bacteria on farms. However, little is known about the current prevalence and molecular ecology of ABR in key opportunistic human pathogens such as Escherichia coli on South American farms. Working with 30 dairy cattle farms and 40 pig farms across two provinces in central-eastern Argentina, we report a comprehensive genomic analysis of third-generation cephalosporin resistance (3GC-R) in E. coli. 3GC-R isolates were recovered from 34.8% (cattle) and 47.8% (pigs) of samples from faecally contaminated sites. Phylogenetic analysis revealed substantial diversity suggestive of long-term horizontal transmission of 3GC-R mechanisms. Despite this, mechanisms such as CTX-M-15 and CTX-M-2 were detected more often in dairy farms, while CTX-M-8 and CMY-2, and co-carriage of amoxicillin/clavulanate resistance and florfenicol resistance were more commonly detected in pig farms. This suggests different selective pressures of antibiotic use in these two animal types, particularly the balance of fourth-versus third-generation cephalosporin use, and of amoxicillin/clavulanate and florfenicol use. We identified the {beta}-lactamase gene blaROB in 3GC-R E. coli, which has previously only been reported in the family Pasteurellaceae, including farmed animal pathogens. blaROB was found alongside a novel florfenicol resistance gene - ydhC - also mobilised from a pig pathogen as part of a new plasmid-mediated composite transposon, which is already widely disseminated. These data set a baseline from which to measure the effects of interventions aimed at reducing on-farm ABR and provide an opportunity to investigate zoonotic transmission of resistant bacteria in this region. ImportanceLittle is known about the ecology of critically important antibiotic resistance among opportunistic human pathogens (e.g. Escherichia coli) on South American farms. By studying 70 farms in central-eastern Argentina, we identified that third-generation cephalosporin resistance (3GC-R) in E. coli was mediated by mechanisms seen more often in certain species (pigs or dairy cattle) and that 3GC-R pig E. coli were more likely to be co-resistant to florfenicol and amoxicillin/clavulanate. This suggests that on-farm antibiotic usage is key to selecting the types of E. coli present on these farms. 3GC-R E. coli were highly phylogenetically variable and we identified the de novo mobilisation of the resistance gene blaROB, alongside a novel florfenicol resistance gene, from pig pathogens into E. coli on a mobile genetic element that was widespread in the study region. Overall, this shows the importance of surveying poorly studied regions for critically important antibiotic resistance which might impact human health.

microbiology↗

UFMTrack: Under-Flow Migration Tracker enabling analysis of the entire multi-step immune cell extravasation cascade across the blood-brain barrier in microfluidic devices

The endothelial blood-brain barrier (BBB) strictly controls immune cell trafficking into the central nervous system (CNS). In neuroinflammatory diseases such as multiple sclerosis, this tight control is, however, disturbed, leading to immune cell infiltration into the CNS. The development of in vitro models of the BBB combined with microfluidic devices has advanced our understanding of the cellular and molecular mechanisms mediating the multi-step T-cell extravasation across the BBB. A major bottleneck of these in vitro studies is the absence of a robust and automated pipeline suitable for analyzing and quantifying the sequential interaction steps of different immune cell subsets with the BBB under physiological flow in vitro. Here we present the Under-Flow Migration Tracker (UFMTrack) framework for studying immune cell interactions with endothelial monolayers under physiological flow. We then showcase a pipeline built based on it to study the entire multi-step extravasation cascade of immune cells across brain microvascular endothelial cells under physiological flow in vitro. UFMTrack achieves 90% track reconstruction efficiency and allows for scaling due to the reduction of the analysis cost and by eliminating experimenter bias. This allowed for an in-depth analysis of all behavioral regimes involved in the multi-step immune cell extravasation cascade. The study summarizes how UFMTrack can be employed to delineate the interactions of CD4+ and CD8+ T cells with the BBB under physiological flow. We also demonstrate its applicability to the other BBB models, showcasing broader applicability of the developed framework to a range of immune cell-endothelial monolayer interaction studies. The UFMTrack framework along with the generated datasets is publicly available in the corresponding repositories. Author summaryImmune cells continuously travel through our body to perform immune surveillance. They travel within blood vessels at a very high speed and slow down upon reaching their target organ by the sequential interaction with different adhesion and signaling molecules on the vascular endothelial cells. The study of molecular mechanisms mediating this multi-step extravasation of immune cells has been significantly advanced by in vitro cultures of microvascular endothelial cell monolayers. The dynamic interaction of the immune cells with endothelial monolayers can be imaged over time in vitro in microfluidic devices under physiological flow. The 2-dimensional structure of the endothelial monolayer allows for reliable visualization of the extravasation process required for the study of the molecular mechanisms involved. The manual analysis of the acquired imaging data is time- consuming and prone to experimenter error. Analysis automation is, however, hampered by the similar appearance of the unlabeled immune and endothelial cells and by the flow causing rapid immune cell displacement. Here we introduce UFMTrack, the under-flow migration tracker framework allowing for automated analysis of immune cell interactions with microvascular endothelial cells under flow in vitro. UFMTrack performs comparably to the manual analysis of an experienced researcher, eliminates experimenters bias, and improves the accuracy of the immune cell tracking. Taken together, UFMTrack sets the stage for scalability of in vitro live cell imaging studies of immune cell extravasation.

immunology↗

Axonal plasticity in response to active forces generated through magnetic nano-pulling

Mechanical force is crucial in guiding axon outgrowth, before and after synapse formation. This process is referred to as "stretch-growth". However, how neurons transduce mechanical inputs into signaling pathways remains poorly understood. Another open question is how stretch-growth is coupled in time with the intercalated addition of new mass along the entire axon. Here, we demonstrate that active mechanical force generated by magnetic nano-pulling induces a remodeling of the axonal cytoskeleton. Specifically, the increase in the axonal density of microtubules leads to an accumulation of organelles and signaling vesicles which, in turn, promotes local translation by increasing the probability of assembly of the "translation factories". The modulation of axonal transport and local translation sustains enhanced axon outgrowth and synapse maturation.

neuroscience↗

Ceftolozane-tazobactam pharmacokinetics during extracorporeal membrane oxygenation in a lung transplant recipient

Ceftolozane-tazobactam pharmacokinetics during extracorporeal membrane oxygenation (ECMO) has not been previously studied. In this work we report on the ceftolozane and tazobactam plasmatic levels in a lung transplant recipient during ECMO, treated with ceftolozane-tazobactam (2g/1g, intravenously every 8 h, 1 h infusion) for a Pseudomonas aeruginosa pulmonary infection. Ceftolozane Cmax and Cmin, monitored during 96 hrs, remained above 60 and 20 g/mL, respectively, with optimal drug exposure (100% %TMIC). Tazobactam levels were above 1.9 g/mL.

pharmacology and toxicology↗

The structure of the Pro-domain of mouse proNGF in contact with the NGF domain

Nerve Growth Factor (NGF) is an important neurotrophic factor involved in the regulation of cell differentiation, maintenance, growth and survival of target neurons. Expressed as a proNGF precursor, NGF is then matured by furin-mediated protease cleavage. Increasing evidence suggests that NGF and proNGF have distinct cellular partners which account for different functional roles. While the structure of mature NGF is available, little is known about the structure of the pro-domain within the context of proNGF because the dynamical and structural features of the protein have so far prevented its structure determination. We have exploited a new hybrid strategy based on nuclear magnetic resonance and modelling validated by small angle X-ray scattering to gain novel insights on the pro-domain, both in isolation and in the context of proNGF. We show that the isolated pro-domain is intrinsically unstructured but has a clear tertiary structure propensity and forms transient tertiary intramolecular contacts. It is also able to interact, albeit weakly, with mature NGF and has per se the ability to induce growth cone collapse, indicating functional independence. Based on paramagnetic relaxation enhancement data and advanced molecular modelling, we have then reconstructed the overall properties of the pro-domain in the context of proNGF and showed that it has a compact structure. Our data represent an important step towards the structural and functional characterization of the properties of proNGF and its pro-domain.

biophysics↗

Activity-dependent expression of reporter proteins at dendritic spines for synaptic activity mapping and optogenetic stimulation

Increasing evidence points to the importance of dendritic spines in the formation and allocation of memories, and alterations of spine number and physiology are associated to memory and cognitive disorders. Synaptic connections and pathways constitute the physical substrate that conveys information in the brain, and different combinations of active synaptic connections are believed to be responsible for the encoding of specific memories. In addition, modifications of the activity of such subsets of synapses are believed to be crucial for memory establishment, but a way to directly test this hypothesis, by selectively controlling the activity of potentiated spines, is currently lagging behind. Therefore it would be important to develop methods to tag active synapses for mapping functionally active connections and to selectively stimulate or interfere with active synapses. Here we introduce an approach to express light-sensitive membrane channels at synapses in an activity-dependent way by means of RNA and protein regulatory sequences. This approach is based on the local expression of reporter proteins, including optogenetic probes, at activated synapses and will allow the mapping of previously active synapses and the re-activation of the neuron only at these sites. This will allow extending the investigation of memory processes beyond the current neuron tagging technologies, whose resolution is limited at the cellular scale. Thus, it will be possible to unveil and recall the synaptic engram out of the global set of synapses.

neuroscience↗