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Abstract Psoriasis is a multifactorial, chronic inflammatory skin disease with unresolved questions on its primary events. Iron overload has been described in the epidermis of psoriasis patients, but its relevance remains unknown. We found that the key iron regulatory hormone hepcidin was highly expressed in the epidermis of psoriasis patients, especially the pustular variants resistant to treatments. In a murine model of acute skin inflammation, keratinocyte-derived hepcidin was required for iron retention in keratinocytes, leading to hyperproliferation of the epidermal layer and neutrophil recruitment, two main features of psoriatic skin lesions. Keratinocytes overexpressing hepcidin were sufficient to elicit these psoriasiform features in a transgenic mouse model. Furthermore, transcriptome analysis of these keratinocytes revealed canonical pathways found in human psoriasis, pointing to a causal role for hepcidin in the pathogenesis of the disease. Altogether, our data suggest that hepcidin could be an actionable target for skin psoriasis treatment, in addition to current therapeutics, or targeted as maintenance therapy during remission to prevent recurrence.

Abstract Hypoxic-ischaemic encephalopathy (HIE) arises from diminished blood flow and oxygen to the neonatal brain during labor, leading to infant mortality or severe brain damage, with a global incidence of 1.5 per 1000 live births. Glucagon-like Peptide 1 Receptor (GLP1-R) agonists, used in type 2 diabetes treatment, exhibit neuroprotective effects in various brain injury models, including HIE. In this study, we observed enhanced neurological outcomes in post-natal day 10 mice with surgically induced hypoxic-ischaemic (HI) brain injury after immediate systemic administration of exendin-4 or semaglutide. Short- and long-term assessments revealed improved neuropathology, survival rates, and locomotor function. We explored the mechanisms by which GLP1-R agonists trigger neuroprotection and reduce inflammation following oxygen-glucose deprivation and HI in neonatal mice, highlighting the upregulation of the PI3/AKT signalling pathway and increased cAMP levels. These findings shed light on the neuroprotective and anti-inflammatory effects of GLP1-R agonists in HIE, potentially extending to other neurological conditions, supporting their potential clinical use in treating infants with HIE.

Abstract Background G-quadruplex DNA (G4) is a non-canonical structure forming in guanine-rich regions, which play a vital role in cancer biology and are now being acknowledged in both nuclear and mitochondrial (mt) genome. However, the impact of G4-based targeted therapy on both nuclear and mt genome, affecting mt function and its underlying mechanisms remain largely unexplored. Methods The mechanisms of action and therapeutic effects of a G4-binding platinum(II) complex, Pt-ttpy, on mitochondria were conducted through a comprehensive approaches with in vitro and in vivo models, including ICP-MS for platinum measurement, PCR-based genetic analysis, western blotting (WB), confocal microscope for mt morphology study, extracellular flux analyzer, JC1 and Annexin V apoptosis assay, flow cytometry and high content microscope screening with single-cell quantification of both ROS and mt specific ROS, as well as click-chemistry for IF study of mt translation. Decipher Pt-ttpy effects on nuclear-encoded mt related genes expression were undertaken via RNA-seq, Chip-seq and CUT-RUN assays. Results Pt-ttpy, shows a highest accumulation in the mitochondria of A2780 cancer cells as compared with two other platinum(II) complexes with no/weak G4-binding properties, Pt-tpy and cisplatin. Pt-ttpy induces mtDNA deletion, copy reduction and transcription inhibition, hindering mt protein translation. Functional analysis reveals potent mt dysfunction without reactive oxygen species (ROS) induction. Mechanistic study provided first evidence that most of mt ribosome genes are highly enriched in G4 structures in their promoter regions, notably, Pt-ttpy impairs most nuclear-encoded mt ribosome genes’ transcription through dampening the recruiting of transcription initiation and elongation factors of NELFB and TAF1 to their promoter with G4-enriched sequences. In vivo studies show Pt-ttpy’s efficient anti-tumor effects, disrupting mt genome function with fewer side effects than cisplatin. Conclusion This study underscores Pt-ttpy as a G4-binding platinum(II) complex, effectively targeting cancer mitochondria through dual action on mt and nuclear G4-enriched genomes without inducing ROS, offering promise for safer and effective platinum-based G4-targeted cancer therapy. Graphical Abstract

Diffusion Magnetic Resonance Imaging tractography is a non-invasive technique that produces a collection of streamlines representing the main white matter bundle trajectories. Methods, such as fiber clustering algorithms, are important in computational neuroscience and have been the basis of several white matter analysis methods and studies. Nevertheless, these clustering methods face the challenge of the absence of ground truth of white matter fibers, making their evaluation difficult. As an alternative solution, we present an innovative brain fiber bundle simulator that uses spline curves for fiber representation. The methodology uses a tubular model for the bundle simulation based on a bundle centroid and five radii along the bundle. The algorithm was tested by simulating 28 Deep White Matter atlas bundles, leading to low inter-bundle distances and high intersection percentages between the original and simulated bundles. To prove the utility of the simulator, we created three whole-brain datasets containing different numbers of fiber bundles to assess the quality performance of QuickBundles and Fast Fiber Clustering algorithms using five clustering metrics. Our results indicate that QuickBundles tends to split less and Fast Fiber Clustering tends to merge less, which is consistent with their expected behavior. The performance of both algorithms decreases when the number of bundles is increased due to higher bundle crossings. Additionally, the two algorithms exhibit robust behavior with input data permutation. To our knowledge, this is the first whole-brain fiber bundle simulator capable of assessing fiber clustering algorithms with realistic data.

There is an urgent need for alternative therapies targeting human dendritic cells (DCs) that could reverse inflammatory syndromes in many autoimmune and inflammatory diseases and organ transplantations. Here, we describe a bispecific antibody (bsAb) strategy tethering two pathogen-recognition receptors at the surface of human DCs. This cross-linking switches DCs into a tolerant profile able to induce regulatory T-cell differentiation. The bsAbs, not parental Abs, induced interleukin 10 and transforming growth factor β1 secretion in monocyte-derived DCs and human peripheral blood mononuclear cells. In addition, they induced interleukin 10 secretion by synovial fluid cells in rheumatoid arthritis and gout patients. This concept of bsAb-induced tethering of surface pathogen-recognition receptors switching cell properties opens a new therapeutic avenue for controlling inflammation and restoring immune tolerance.

We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.

As in all sectors of science and industry, artificial intelligence (AI) is meant to have a high impact in the discovery of antibodies in the coming years. Antibody discovery was traditionally conducted through a succession of experimental steps: animal immunization, screening of relevant clones, in vitro testing, affinity maturation, in vivo testing in animal models, then different steps of humanization and maturation generating the candidate that will be tested in clinical trials. This scheme suffers from different flaws, rendering the whole process very risky, with an attrition rate over 95%. The rise of in silico methods, among which AI, has been gradually proven to reliably guide different experimental steps with more robust processes. They are now capable of covering the whole discovery process. Amongst the players in this new field, the company MAbSilico proposes an in silico pipeline allowing to design antibody sequences in a few days, already humanized and optimized for affinity and developability, considerably de-risking and accelerating the discovery process.

Summary: Background: SARS-CoV-2-neutralizing antibodies (nABs) showed great promise in the early phases of the COVID-19 pandemic. The emergence of resistant strains, however, quickly rendered the majority of clinically approved nABs ineffective. This underscored the imperative to develop nAB cocktails targeting non-overlapping epitopes. Methods: Undertaking a nAB discovery program, we employed a classical workflow, while integrating artificial intelligence (AI)-based prediction to select non-competing nABs very early in the pipeline. We identified and in vivo validated (in female Syrian hamsters) two highly potent nABs. Findings: Despite the promising results, in depth cryo-EM structural analysis demonstrated that the AI-based prediction employed with the intention to ensure non-overlapping epitopes was inaccurate. The two nABs in fact bound to the same receptor-binding epitope in a remarkably similar manner. Interpretation: Our findings indicate that, even in the Alphafold era, AI-based predictions of paratope-epitope interactions are rough and experimental validation of epitopes remains an essential cornerstone of a successful nAB lead selection. Funding: Full list of funders is provided at the end of the manuscript.

BackgroundScience is beginning to establish the benefits of the use of virtual reality (VR) in health care. This therapeutic approach may be an appropriate complementary treatment for some mental illnesses. It could prevent high levels of morbidity and improve the physical health of patients. For many years, the literature has shown the health benefits of physical exercise. Physical exercise in a VR environment may improve the management of mild to moderate mental health conditions. In this context, we developed a virtual environment combined with an ergocycle (the augmented physical training for isolated and confined environments [APTICE] system). ObjectiveThis study aims to investigate the impact of physical exercise in a VR environment. MethodsA total of 14 healthy participants (11 men and 3 women; mean age 43.28, SD 10.60 years) undertook 15 minutes of immersive physical exercise using the system. Measures included mindfulness and immersion disposition, subjective perceptions of sensory information, user experience, and VR experience (ie, psychological state, flow, and presence). ResultsFirst, the APTICE system appears to be a useful tool because the user experience is positive (subscales in the AttrakDiff questionnaire: pragmatic quality=0.99; hedonic quality–stimulation=1.90; hedonic quality–identification=0.67; attractiveness=1.58). Second, the system can induce a positive psychological state (negative emotion, P=.06) and an experience of flow and presence (P values ranging from

Mapping the chimpanzee brain connectome and comparing it to that of humans is key to our understanding of similarities and differences in primate evolution that occurred after the split from their common ancestor around 6 million years ago. In contrast to studies on macaque species’ brains, fewer studies have specifically addressed the structural connectivity of the chimpanzee brain and its comparison with the human brain. Most comparative studies in the literature focus on the anatomy of the cortex and deep nuclei to evaluate how their morphology and asymmetry differ from that of the human brain, and some studies have emerged concerning the study of brain connectivity among humans, monkeys, and apes. In this work, we established a new white matter atlas of the deep and superficial white matter structural connectivity in chimpanzees. In vivo anatomical and diffusion-weighted magnetic resonance imaging (MRI) data were collected on a 3-Tesla MRI system from 39 chimpanzees. These datasets were subsequently processed using a novel fiber clustering pipeline adapted to the chimpanzee brain, enabling us to create two novel deep and superficial white matter connectivity atlases representative of the chimpanzee brain. These atlases provide the scientific community with an important and novel set of reference data for understanding the commonalities and differences in structural connectivity between the human and chimpanzee brains. We believe this study to be innovative both in its novel approach and in mapping the superficial white matter bundles in the chimpanzee brain, which will contribute to a better understanding of hominin brain evolution.