Evolution toward beta common chain receptor usage links the matrix proteins of HIV-1 and its ancestors to human erythropoietin.

The HIV-1 matrix protein p17 (p17) is a pleiotropic molecule impacting on different cell types. Its interaction with many cellular proteins underlines the importance of the viral protein as a major determinant of human specific adaptation. We previously showed the proangiogenic capability of p17. Here, by integrating functional analysis and receptor binding, we identify a functional epitope that displays molecular mimicry with human erythropoietin (EPO) and promotes angiogenesis through common beta chain receptor (ßCR) activation. The functional EPO-like epitope was found to be present in the matrix protein of HIV-1 ancestors SIV originated in chimpanzees (SIVcpz) and gorillas (SIVgor) but not in that of HIV-2 and its ancestor SIVsmm from sooty mangabeys. According to biological data, evolution of the EPO-like epitope showed a clear differentiation between HIV-1/SIVcpz-gor and HIV-2/SIVsmm branches, thus highlighting this epitope on p17 as a divergent signature discriminating HIV-1 and HIV-2 ancestors. P17 is known to enhance HIV-1 replication. Similarly to other ßCR ligands, p17 is capable of attracting and activating HIV-1 target cells and promoting a proinflammatory microenvironment. Thus, it is tempting to speculate that acquisition of an epitope on the matrix proteins of HIV-1 ancestors capable of triggering ßCR may have represented a critical step to enhance viral aggressiveness and early human-to-human SIVcpz/gor dissemination. The hypothesis that the p17/ßCR interaction and ßCR abnormal stimulation may also play a role in sustaining chronic activation and inflammation, thus marking the difference between HIV-1 and HIV-2 in term of pathogenicity, needs further investigation.

Comparison Between a Single-Lead ECG Garment Device and a Holter Monitor: A Signal Quality Assessment.

Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as "Good", 12% as "Acceptable", and 18% as "Not Readable". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

Germline NUP98 Variants in Two Siblings with a Rothmund-Thomson-Like Spectrum: Protein Functional Changes Predicted by Molecular Modeling.

Two adult siblings born to first-cousin parents presented a clinical phenotype reminiscent of Rothmund-Thomson syndrome (RTS), implying fragile hair, absent eyelashes/eyebrows, bilateral cataracts, mottled pigmentation, dental decay, hypogonadism, and osteoporosis. As the clinical suspicion was not supported by the sequencing of RECQL4, the RTS2-causative gene, whole exome sequencing was applied and disclosed the homozygous variants c.83G>A (p.Gly28Asp) and c.2624A>C (p.Glu875Ala) in the nucleoporin 98 (NUP98) gene. Though both variants affect highly conserved amino acids, the c.83G>A looked more intriguing due to its higher pathogenicity score and location of the replaced amino acid between phenylalanine-glycine (FG) repeats within the first NUP98 intrinsically disordered region. Molecular modeling studies of the mutated NUP98 FG domain evidenced a dispersion of the intramolecular cohesion elements and a more elongated conformational state compared to the wild type. This different dynamic behavior may affect the NUP98 functions as the minor plasticity of the mutated FG domain undermines its role as a multi-docking station for RNA and proteins, and the impaired folding can lead to the weakening or the loss of specific interactions. The clinical overlap of NUP98-mutated and RTS2/RTS1 patients, accounted by converging dysregulated gene networks, supports this first-described constitutional NUP98 disorder, expanding the well-known role of NUP98 in cancer.

Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study.

Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.

A Drug Discovery Approach for an Effective Pain Therapy through Selective Inhibition of Nav1.7.

Chronic pain is a widespread disorder affecting millions of people and is insufficiently addressed by current classes of analgesics due to significant long-term or high dosage side effects. A promising approach that was recently proposed involves the systemic inhibition of the voltage-gated sodium channel Nav1.7, capable of cancelling pain perception completely. Notwithstanding numerous attempts, currently no drugs have been approved for the inhibition of Nav1.7. The task is complicated by the difficulty of creating a selective drug for Nav1.7, and avoiding binding to the many human paralogs performing fundamental physiological functions. In our work, we obtained a promising set of ligands with up to 5-40-fold selectivity and reaching 5.2 nanomolar binding affinity by employing a proper treatment of the problem and an innovative differential in silico screening procedure to discriminate for affinity and selectivity against the Nav paralogs. The absorption, distribution, metabolism, and excretion (ADME) properties of our top-scoring ligands were also evaluated, with good to excellent results. Additionally, our study revealed that the top-scoring ligand is a stereoisomer of an already-approved drug. These facts could reduce the time required to bring a new effective and selective Nav1.7 inhibitor to the market.

Virtual Drug Repositioning as a Tool to Identify Natural Small Molecules That Synergize with Lumacaftor in F508del-CFTR Binding and Rescuing.

Cystic fibrosis is a hereditary disease mainly caused by the deletion of the Phe 508 (F508del) of the cystic fibrosis transmembrane conductance regulator (CFTR) protein that is thus withheld in the endoplasmic reticulum and rapidly degraded by the ubiquitin/proteasome system. Cystic fibrosis remains a potentially fatal disease, but it has become treatable as a chronic condition due to some CFTR-rescuing drugs that, when used in combination, increase in their therapeutic effect due to a synergic action. Also, dietary supplementation of natural compounds in combination with approved drugs could represent a promising strategy to further alleviate cystic fibrosis symptoms. On these bases, we screened by in silico drug repositioning 846 small synthetic or natural compounds from the AIFA database to evaluate their capacity to interact with the highly druggable lumacaftor binding site of F508del-CFTR. Among the identified hits, nicotinamide (NAM) was predicted to accommodate into the lumacaftor binding region of F508del-CFTR without competing against the drug but rather stabilizing its binding. The effective capacity of NAM to bind F508del-CFTR in a lumacaftor-uncompetitive manner was then validated experimentally by surface plasmon resonance analysis. Finally, the capacity of NAM to synergize with lumacaftor increasing its CFTR-rescuing activity was demonstrated in cell-based assays. This study suggests the possible identification of natural small molecules devoid of side effects and endowed with the capacity to synergize with drugs currently employed for the treatment of cystic fibrosis, which hopefully will increase the therapeutic efficacy with lower doses.

Are In Silico Approaches Applicable As a First Step for the Prediction of e-Liquid Toxicity in e-Cigarettes?

Recent studies have raised concerns about e-cigarette liquid inhalation toxicity by reporting the presence of chemicals with European Union CLP toxicity classification. In this scenario, the regulatory context is still developing and is not yet up to date with vaping current reality. Due to the paucity of toxicological studies, robust data regarding which components in e-liquids exhibit potential toxicities, are still inconsistent. In this study we applied computational methods for estimating the toxicity of poorly studied chemicals as a useful tool for predicting the acute toxicity of chemicals contained in e-liquids. The purpose of this study was 3-fold: (a) to provide a lower tier assessment of the potential health concerns associated with e-liquid ingredients, (b) to prioritize e-liquid ingredients by calculating the e-tox index, and (c) to estimate acute toxicity of e-liquid mixtures. QSAR models were generated using QSARINS software to fill the acute toxicity data gap of 264 e-liquid ingredients. As a second step, the potential acute toxicity of e-liquids mixtures was evaluated. Our preliminary data suggest that a computational approach may serve as a roadmap to enable regulatory bodies to better regulate e-liquid composition and to contribute to consumer health protection.

Recent Strategic Advances in CFTR Drug Discovery: An Overview.

Cystic fibrosis transmembrane conductance regulator (CFTR)-rescuing drugs have already transformed cystic fibrosis (CF) from a fatal disease to a treatable chronic condition. However, new-generation drugs able to bind CFTR with higher specificity/affinity and to exert stronger therapeutic benefits and fewer side effects are still awaited. Computational methods and biosensors have become indispensable tools in the process of drug discovery for many important human pathologies. Instead, they have been used only piecemeal in CF so far, calling for their appropriate integration with well-tried CF biochemical and cell-based models to speed up the discovery of new CFTR-rescuing drugs. This review will give an overview of the available structures and computational models of CFTR and of the biosensors, biochemical and cell-based assays already used in CF-oriented studies. It will also give the reader some insights about how to integrate these tools as to improve the efficiency of the drug discovery process targeted to CFTR.

Genome-based analysis for the identification of genes involved in o-xylene degradation in Rhodococcus opacus R7.

BACKGROUND: Bacteria belonging to the Rhodococcus genus play an important role in the degradation of many contaminants, including methylbenzenes. These bacteria, widely distributed in the environment, are known to be a powerhouse of numerous degradation functions, due to their ability to metabolize a wide range of organic molecules including aliphatic, aromatic, polycyclic aromatic compounds (PAHs), phenols, and nitriles. In accordance with their immense catabolic diversity, Rhodococcus spp. possess large and complex genomes, which contain a multiplicity of catabolic genes, a high genetic redundancy of biosynthetic pathways and a sophisticated regulatory network. The present study aimed to identify genes involved in the o-xylene degradation in R. opacus strain R7 through a genome-based approach. RESULTS: Using genome-based analysis we identified all the sequences in the R7 genome annotated as dioxygenases or monooxygenases/hydroxylases and clustered them into two different trees. The akb, phe and prm sequences were selected as genes encoding respectively for dioxygenases, phenol hydroxylases and monooxygenases and their putative involvement in o-xylene oxidation was evaluated. The involvement of the akb genes in o-xylene oxidation was demonstrated by RT-PCR/qPCR experiments after growth on o-xylene and by the selection of the R7-50 leaky mutant. Although the akb genes are specifically activated for o-xylene degradation, metabolic intermediates of the pathway suggested potential alternative oxidation steps, possibly through monooxygenation. This led us to further investigate the role of the prm and the phe genes. Results showed that these genes were transcribed in a constitutive manner, and that the activity of the Prm monooxygenase was able to transform o-xylene slowly in intermediates as 3,4-dimethylphenol and 2-methylbenzylalcohol. Moreover, the expression level of phe genes, homologous to the phe genes of Rhodococcus spp. 1CP and UPV-1 with a 90% identity, could explain their role in the further oxidation of o-xylene and R7 growth on dimethylphenols. CONCLUSIONS: These results suggest that R7 strain is able to degrade o-xylene by the Akb dioxygenase system leading to the production of the corresponding dihydrodiol. Likewise, the redundancy of sequences encoding for several monooxygenases/phenol hydroxylases, supports the involvement of other oxygenases converging in the o-xylene degradation pathway in R7 strain.

Speeding Up the Identification of Cystic Fibrosis Transmembrane Conductance Regulator-Targeted Drugs: An Approach Based on Bioinformatics Strategies and Surface Plasmon Resonance.

Cystic fibrosis (CF) is mainly caused by the deletion of Phe 508 (ΔF508) in the cystic fibrosis transmembrane conductance regulator (CFTR) protein that is thus withheld in the endoplasmic reticulum and rapidly degraded by the ubiquitin/proteasome system. New drugs able to rescue ΔF508-CFTR trafficking are eagerly awaited. An integrated bioinformatics and surface plasmon resonance (SPR) approach was here applied to investigate the rescue mechanism(s) of a series of CFTR-ligands including VX809, VX770 and some aminoarylthiazole derivatives (AAT). Computational studies tentatively identified a large binding pocket in the ΔF508-CFTR nucleotide binding domain-1 (NBD1) and predicted all the tested compounds to bind to three sub-regions of this main pocket. Noticeably, the known CFTR chaperone keratin-8 (K8) seems to interact with some residues located in one of these sub-pockets, potentially interfering with the binding of some ligands. SPR results corroborated all these computational findings. Moreover, for all the considered ligands, a statistically significant correlation was determined between their binding capability to ΔF508-NBD1 measured by SPR and the pockets availability measured by computational studies. Taken together, these results demonstrate a strong agreement between the in silico prediction and the SPR-generated binding data, suggesting a path to speed up the identification of new drugs for the treatment of cystic fibrosis.

Removing duplicate reads using graphics processing units.

BACKGROUND: During library construction polymerase chain reaction is used to enrich the DNA before sequencing. Typically, this process generates duplicate read sequences. Removal of these artifacts is mandatory, as they can affect the correct interpretation of data in several analyses. Ideally, duplicate reads should be characterized by identical nucleotide sequences. However, due to sequencing errors, duplicates may also be nearly-identical. Removing nearly-identical duplicates can result in a notable computational effort. To deal with this challenge, we recently proposed a GPU method aimed at removing identical and nearly-identical duplicates generated with an Illumina platform. The method implements an approach based on prefix-suffix comparison. Read sequences with identical prefix are considered potential duplicates. Then, their suffixes are compared to identify and remove those that are actually duplicated. Although the method can be efficiently used to remove duplicates, there are some limitations that need to be overcome. In particular, it cannot to detect potential duplicates in the event that prefixes are longer than 27 bases, and it does not provide support for paired-end read libraries. Moreover, large clusters of potential duplicates are split into smaller with the aim to guarantees a reasonable computing time. This heuristic may affect the accuracy of the analysis. RESULTS: In this work we propose GPU-DupRemoval, a new implementation of our method able to (i) cluster reads without constraints on the maximum length of the prefixes, (ii) support both single- and paired-end read libraries, and (iii) analyze large clusters of potential duplicates. CONCLUSIONS: Due to the massive parallelization obtained by exploiting graphics cards, GPU-DupRemoval removes duplicate reads faster than other cutting-edge solutions, while outperforming most of them in terms of amount of duplicates reads.

Exome sequencing identifies variants in two genes encoding the LIM-proteins NRAP and FHL1 in an Italian patient with BAG3 myofibrillar myopathy.

Myofibrillar myopathies (MFMs) are genetically heterogeneous dystrophies characterized by the disintegration of Z-disks and myofibrils and are associated with mutations in genes encoding Z-disk or Z-disk-related proteins. The c.626 C > T (p.P209L) mutation in the BAG3 gene has been described as causative of a subtype of MFM. We report a sporadic case of a 26-year-old Italian woman, affected by MFM with axonal neuropathy, cardiomyopathy, rigid spine, who carries the c.626 C > T mutation in the BAG3 gene. The patient and her non-consanguineous healthy parents and brother were studied with whole exome sequencing (WES) to further investigate the genetic basis of this complex phenotype. In the patient, we found that the BAG3 mutation is associated with variants in the NRAP and FHL1 genes that encode muscle-specific, LIM domain containing proteins. Quantitative real time PCR, immunohistochemistry and Western blot analysis of the patient's muscular biopsy showed the absence of NRAP expression and FHL1 accumulation in aggregates in the affected skeletal muscle tissue. Molecular dynamic analysis of the mutated FHL1 domain showed a modification in its surface charge, which could affect its capability to bind its target proteins. To our knowledge this is the first study reporting, in a BAG3 MFM, the simultaneous presence of genetic variants in the BAG3 and FHL1 genes (previously described as independently associated with MFMs) and linking the NRAP gene to MFM for the first time.

A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units.

BACKGROUND: Single Nucleotide Polymorphism (SNP) genotyping analysis is very susceptible to SNPs chromosomal position errors. As it is known, SNPs mapping data are provided along the SNP arrays without any necessary information to assess in advance their accuracy. Moreover, these mapping data are related to a given build of a genome and need to be updated when a new build is available. As a consequence, researchers often plan to remap SNPs with the aim to obtain more up-to-date SNPs chromosomal positions. In this work, we present G-SNPM a GPU (Graphics Processing Unit) based tool to map SNPs on a genome. METHODS: G-SNPM is a tool that maps a short sequence representative of a SNP against a reference DNA sequence in order to find the physical position of the SNP in that sequence. In G-SNPM each SNP is mapped on its related chromosome by means of an automatic three-stage pipeline. In the first stage, G-SNPM uses the GPU-based short-read mapping tool SOAP3-dp to parallel align on a reference chromosome its related sequences representative of a SNP. In the second stage G-SNPM uses another short-read mapping tool to remap the sequences unaligned or ambiguously aligned by SOAP3-dp (in this stage SHRiMP2 is used, which exploits specialized vector computing hardware to speed-up the dynamic programming algorithm of Smith-Waterman). In the last stage, G-SNPM analyzes the alignments obtained by SOAP3-dp and SHRiMP2 to identify the absolute position of each SNP. RESULTS AND CONCLUSIONS: To assess G-SNPM, we used it to remap the SNPs of some commercial chips. Experimental results shown that G-SNPM has been able to remap without ambiguity almost all SNPs. Based on modern GPUs, G-SNPM provides fast mappings without worsening the accuracy of the results. G-SNPM can be used to deal with specialized Genome Wide Association Studies (GWAS), as well as in annotation tasks that require to update the SNP mapping probes.

Guidelines for managing data and processes in bone and cartilage tissue engineering.

BACKGROUND: In the last decades, a wide number of researchers/clinicians involved in tissue engineering field published several works about the possibility to induce a tissue regeneration guided by the use of biomaterials. To this aim, different scaffolds have been proposed, and their effectiveness tested through in vitro and/or in vivo experiments. In this context, integration and meta-analysis approaches are gaining importance for analyses and reuse of data as, for example, those concerning the bone and cartilage biomarkers, the biomolecular factors intervening in cell differentiation and growth, the morphology and the biomechanical performance of a neo-formed tissue, and, in general, the scaffolds' ability to promote tissue regeneration. Therefore standards and ontologies are becoming crucial, to provide a unifying knowledge framework for annotating data and supporting the semantic integration and the unambiguous interpretation of novel experimental results. RESULTS: In this paper a conceptual framework has been designed for bone/cartilage tissue engineering domain, by now completely lacking standardized methods. A set of guidelines has been provided, defining the minimum information set necessary for describing an experimental study involved in bone and cartilage regenerative medicine field. In addition, a Bone/Cartilage Tissue Engineering Ontology (BCTEO) has been developed to provide a representation of the domain's concepts, specifically oriented to cells, and chemical composition, morphology, physical characterization of biomaterials involved in bone/cartilage tissue engineering research. CONCLUSIONS: Considering that tissue engineering is a discipline that traverses different semantic fields and employs many data types, the proposed instruments represent a first attempt to standardize the domain knowledge and can provide a suitable means to integrate data across the field.

Molecular interaction studies of HIV-1 matrix protein p17 and heparin: identification of the heparin-binding motif of p17 as a target for the development of multitarget antagonists.

Once released by HIV(+) cells, p17 binds heparan sulfate proteoglycans (HSPGs) and CXCR1 on leukocytes causing their dysfunction. By exploiting an approach integrating computational modeling, site-directed mutagenesis of p17, chemical desulfation of heparin, and surface plasmon resonance, we characterized the interaction of p17 with heparin, a HSPG structural analog, and CXCR1. p17 binds to heparin with an affinity (K(d) = 190 nm) that is similar to those of other heparin-binding viral proteins. Two stretches of basic amino acids (basic motifs) are present in p17 N and C termini. Neutralization (Arg→Ala substitution) of the N-terminal, but not of the C-terminal basic motif, causes the loss of p17 heparin-binding capacity. The N-terminal heparin-binding motif of p17 partially overlaps the CXCR1-binding domain. Accordingly, its neutralization prevents also p17 binding to the chemochine receptor. Competition experiments demonstrated that free heparin and heparan sulfate (HS), but not selectively 2-O-, 6-O-, and N-O desulfated heparins, prevent p17 binding to substrate-immobilized heparin, indicating that the sulfate groups of the glycosaminoglycan mediate p17 interaction. Evaluation of the p17 antagonist activity of a panel of biotechnological heparins derived by chemical sulfation of the Escherichia coli K5 polysaccharide revealed that the highly N,O-sulfated derivative prevents the binding of p17 to both heparin and CXCR1, thus inhibiting p17-driven chemotactic migration of human monocytes with an efficiency that is higher than those of heparin and HS. Here, we characterized at a molecular level the interaction of p17 with its cellular receptors, laying the basis for the development of heparin-mimicking p17 antagonists.

Corrigendum to targeted therapy and deep learning insights into microglia modulation for spinal cord injury.

[This corrects the article DOI: 10.1016/j.mtbio.2024.101117.].

Targeted therapy and deep learning insights into microglia modulation for spinal cord injury.

Spinal cord injury (SCI) is a devastating condition that can cause significant motor and sensory impairment. Microglia, the central nervous system's immune sentinels, are known to be promising therapeutic targets in both SCI and neurodegenerative diseases. The most effective way to deliver medications and control microglial inflammation is through nanovectors; however, because of the variability in microglial morphology and the lack of standardized techniques, it is still difficult to precisely measure their activation in preclinical models. This problem is especially important in SCI, where the intricacy of the glia response following traumatic events necessitates the use of a sophisticated method to automatically discern between various microglial cell activation states that vary over time and space as the secondary injury progresses. We address this issue by proposing a deep learning-based technique for quantifying microglial activation following drug-loaded nanovector treatment in a preclinical SCI model. Our method uses a convolutional neural network to segment and classify microglia based on morphological characteristics. Our approach's accuracy and efficiency are demonstrated through evaluation on a collection of histology pictures from injured and intact spinal cords. This robust computational technique has potential for analyzing microglial activation across various neuropathologies and demonstrating the usefulness of nanovectors in modifying microglia in SCI and other neurological disorders. It has the ability to speed development in this crucial sector by providing a standardized and objective way to compare therapeutic options.

Synergistic Pharmacological Therapy to Modulate Glial Cells in Spinal Cord Injury.

Current treatments for modulating the glial-mediated inflammatory response after spinal cord injury (SCI) have limited ability to improve recovery. This is quite likely due to the lack of a selective therapeutic approach acting on microgliosis and astrocytosis, the glia components most involved after trauma, while maximizing efficacy and minimizing side effects. A new nanogel that can selectively release active compounds in microglial cells and astrocytes is developed and characterized. The degree of selectivity and subcellular distribution of the nanogel is evaluated by applying an innovative super-resolution microscopy technique, expansion microscopy. Two different administration schemes are then tested in a SCI mouse model: in an early phase, the nanogel loaded with Rolipram, an anti-inflammatory drug, achieves significant improvement in the animal's motor performance due to the increased recruitment of microglia and macrophages that are able to localize the lesion. Treatment in the late phase, however, gives opposite results, with worse motor recovery because of the widespread degeneration. These findings demonstrate that the nanovector can be selective and functional in the treatment of the glial component in different phases of SCI. They also open a new therapeutic scenario for tackling glia-mediated inflammation after neurodegenerative events in the central nervous system.

Hepatic and adipose tissue transcriptome analysis highlights a commonly deregulated autophagic pathway in severe MASLD.

OBJECTIVE: The incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly ramping up due to the spread of obesity, which is characterized by expanded and dysfunctional visceral adipose tissue (VAT). Previous studies have investigated the hepatic transcriptome across MASLD, whereas few studies have focused on VAT. METHODS: We performed RNA sequencing in 167 hepatic samples from patients with obesity and in a subset of 79 matched VAT samples. Circulating cathepsin D (CTSD), a lysosomal protease, was measured by ELISA, whereas the autophagy-lysosomal pathway was assessed by Western blot in hepatic and VAT samples (n = 20). RESULTS: Inflammation, extracellular matrix remodeling, and mitochondrial dysfunction were upregulated in severe MASLD in both tissues, whereas autophagy and oxidative phosphorylation were reduced. Tissue comparative analysis revealed 13 deregulated genes, including CTSD, which showed the most robust diagnostic accuracy in discriminating mild and severe MASLD. CTSD expression correlated with circulating protein, whose increase was further validated in 432 histologically characterized MASLD patients, showing a high accuracy in foreseeing severe liver injury. In addition, the assessment of serum CTSD increased the performance of fibrosis 4 in diagnosing advanced disease. CONCLUSIONS: By comparing the hepatic and VAT transcriptome during MASLD, we refined the concept by which CTSD may represent a potential biomarker of severe disease.