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.

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.

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.

Ten Years of Experience With a Telemedicine Platform Dedicated to Health Care Personnel: Implementation Report.

BACKGROUND: Telemedicine, a term that encompasses several applications and tasks, generally involves the remote management and treatment of patients by physicians. It is known as transversal telemedicine when practiced among health care professionals (HCPs). OBJECTIVE: We describe the experience of implementing our telemedicine Eumeda platform for HCPs over the last 10 years. METHODS: A web-based informatics platform was developed that had continuously updated hypertext created using advanced technology and the following features: security, data insertion, dedicated software for image analysis, and the ability to export data for statistical surveys. Customizable files called "modules" were designed and built for different fields of medicine, mainly in the ophthalmology subspecialty. Each module was used by HCPs with different authorization profiles. IMPLEMENTATION (RESULTS): Twelve representative modules for different projects are presented in this manuscript. These modules evolved over time, with varying degrees of interconnectivity, including the participation of a number of centers in 19 cities across Italy. The number of HCP operators involved in each single module ranged from 6 to 114 (average 21.8, SD 28.5). Data related to 2574 participants were inserted across all the modules. The average percentage of completed text/image fields in the 12 modules was 65.7%. All modules were evaluated in terms of access, acceptability, and medical efficacy. In their final evaluation, the participants judged the modules to be useful and efficient for clinical use. CONCLUSIONS: Our results demonstrate the usefulness of the telemedicine platform for HCPs in terms of improved knowledge in medicine, patient care, scientific research, teaching, and the choice of therapies. It would be useful to start similar projects across various health care fields, considering that in the near future medicine as we know it will completely change.

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.

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.

High-Accuracy ncRNA Function Prediction via Deep Learning Using Global and Local Sequence Information.

The prediction of the biological function of non-coding ribonucleic acid (ncRNA) is an important step towards understanding the regulatory mechanisms underlying many diseases. Since non-coding RNAs are present in great abundance in human cells and are functionally diverse, developing functional prediction tools is necessary. With recent advances in non-coding RNA biology and the availability of complete genome sequences for a large number of species, we now have a window of opportunity for studying non-coding RNA biology. However, the computational methods used to predict the non-coding RNA functions are mostly either scarcely accurate, when based on sequence information alone, or prohibitively expensive in terms of computational burden when a secondary structure prediction is needed. We propose a novel computational method to predict the biological function of non-coding RNA genes that is based on a collection of deep network architectures utilizing solely ncRNA sequence information and which does not rely on or require expensive secondary ncRNA structure information. The approach presented in this work exhibits comparable or superior accuracy to methods that employ both sequence and structural features, at a much lower computational cost.

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.

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.

Genome-Based Exploration of Rhodococcus Species for Plastic-Degrading Genetic Determinants Using Bioinformatic Analysis.

Plastic polymer waste management is an increasingly prevalent issue. In this paper, Rhodococcus genomes were explored to predict new plastic-degrading enzymes based on recently discovered biodegrading enzymes for diverse plastic polymers. Bioinformatics prediction analyses were conducted using 124 gene products deriving from diverse microorganisms retrieved from databases, literature data, omic-approaches, and functional analyses. The whole results showed the plastic-degrading potential of Rhodococcus genus. Among the species with high plastic-degrading potential, R. erythropolis, R. equi, R. opacus, R. qingshengii, R. fascians, and R. rhodochrous appeared to be the most promising for possible plastic removal. A high number of genetic determinants related to polyester biodegradation were obtained from different Rhodococcus species. However, score calculation demonstrated that Rhodococcus species (especially R. pyridinivorans, R. qingshengii, and R. hoagii) likely possess PE-degrading enzymes. The results identified diverse oxidative systems, including multicopper oxidases, alkane monooxygenases, cytochrome P450 hydroxylases, para-nitrobenzylesterase, and carboxylesterase, and they could be promising reference sequences for the biodegradation of plastics with C-C backbone, plastics with heteroatoms in the main chain, and polyesters, respectively. Notably, the results of this study could be further exploited for biotechnological applications in biodegradative processes using diverse Rhodococcus strains and through catalytic reactions.

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.

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 Boron Delivery Antibody (BDA) with Boronated Specific Residues: New Perspectives in Boron Neutron Capture Therapy from an In Silico Investigation.

Boron Neutron Capture Therapy (BNCT) is a tumor cell-selective radiotherapy based on a nuclear reaction that occurs when the isotope boron-10 (10B) is radiated by low-energy thermal neutrons or epithermal neutrons, triggering a nuclear fission response and enabling a selective administration of irradiation to cells. Hence, we need to create novel delivery agents containing 10B with high tumor selectivity, but also exhibiting low intrinsic toxicity, fast clearance from normal tissue and blood, and no pharmaceutical effects. In the past, boronated monoclonal antibodies have been proposed using large boron-containing molecules or dendrimers, but with no investigations in relation to maintaining antibody specificity and structural and functional features. This work aims at improving the potential of monoclonal antibodies applied to BNCT therapy, identifying in silico the best native residues suitable to be substituted with a boronated one, carefully evaluating the effect of boronation on the 3D structure of the monoclonal antibody and on its binding affinity. A boronated monoclonal antibody was thus generated for specific 10B delivery. In this context, we have developed a case study of Boron Delivery Antibody Identification Pipeline, which has been tested on cetuximab. Cetuximab is an epidermal growth factor receptor (EGFR) inhibitor used in the treatment of metastatic colorectal cancer, metastatic non-small cell lung cancer, and head and neck cancer.

Transcriptomic analysis of Rhodococcus opacus R7 grown on polyethylene by RNA-seq.

Plastic waste management has become a global issue. Polyethylene (PE) is the most abundant synthetic plastic worldwide, and one of the most resistant to biodegradation. Indeed, few bacteria can degrade polyethylene. In this paper, the transcriptomic analysis unveiled for the first time Rhodococcus opacus R7 complex genetic system based on diverse oxidoreductases for polyethylene biodegradation. The RNA-seq allowed uncovering genes putatively involved in the first step of oxidation. In-depth investigations through preliminary bioinformatic analyses and enzymatic assays on the supernatant of R7 grown in the presence of PE confirmed the activation of genes encoding laccase-like enzymes. Moreover, the transcriptomic data allowed identifying candidate genes for the further steps of short aliphatic chain oxidation including alkB gene encoding an alkane monooxygenase, cyp450 gene encoding cytochrome P450 hydroxylase, and genes encoding membrane transporters. The PE biodegradative system was also validated by FTIR analysis on R7 cells grown on polyethylene.

A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer.

OBJECTIVE: Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH). METHODS: We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy. RESULTS: We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1-2 and 4-5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3). CONCLUSIONS: A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies.

Genetic Determinants in a Critical Domain of NS5A Correlate with Hepatocellular Carcinoma in Cirrhotic Patients Infected with HCV Genotype 1b.

HCV is an important cause of hepatocellular carcinoma (HCC). HCV NS5A domain-1 interacts with cellular proteins inducing pro-oncogenic pathways. Thus, we explore genetic variations in NS5A domain-1 and their association with HCC, by analyzing 188 NS5A sequences from HCV genotype-1b infected DAA-naïve cirrhotic patients: 34 with HCC and 154 without HCC. Specific NS5A mutations significantly correlate with HCC: S3T (8.8% vs. 1.3%, p = 0.01), T122M (8.8% vs. 0.0%, p < 0.001), M133I (20.6% vs. 3.9%, p < 0.001), and Q181E (11.8% vs. 0.6%, p < 0.001). By multivariable analysis, the presence of >1 of them independently correlates with HCC (OR (95%CI): 21.8 (5.7-82.3); p < 0.001). Focusing on HCC-group, the presence of these mutations correlates with higher viremia (median (IQR): 5.7 (5.4-6.2) log IU/mL vs. 5.3 (4.4-5.6) log IU/mL, p = 0.02) and lower ALT (35 (30-71) vs. 83 (48-108) U/L, p = 0.004), suggesting a role in enhancing viral fitness without affecting necroinflammation. Notably, these mutations reside in NS5A regions known to interact with cellular proteins crucial for cell-cycle regulation (p53, p85-PIK3, and ß-catenin), and introduce additional phosphorylation sites, a phenomenon known to ameliorate NS5A interaction with cellular proteins. Overall, these results provide a focus for further investigations on molecular bases of HCV-mediated oncogenesis. The role of theseNS5A domain-1 mutations in triggering pro-oncogenic stimuli that can persist also despite achievement of sustained virological response deserves further investigation.

In silico drug repositioning on F508del-CFTR: A proof-of-concept study on the AIFA library.

Computational drug repositioning is of growing interest to academia and industry, for its ability to rapidly screen a huge number of candidates in silico (exploiting comprehensive drug datasets) together with reduced development cost and time. The potential of drug repositioning has not been fully evaluated yet for cystic fibrosis (CF), a disease mainly caused by deletion of Phe 508 (F508del) of the cystic fibrosis transmembrane conductance regulator (CFTR) protein. F508del-CFTR is thus withheld in the endoplasmic reticulum and rapidly degraded by the ubiquitin/proteasome system. CF is still a fatal disease. Nowadays, it is treatable by some CFTR-rescuing drugs, but new-generation drugs with stronger therapeutic benefits and fewer side effects are still awaited. In this manuscript we report about the results of a pilot computational drug repositioning screening in search of F508del-CFTR-targeted drugs performed on AIFA library by means of a dedicated computational pipeline and surface plasmon resonance binding assay to experimentally validate the computational findings.

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.

B-cell clonogenic activity of HIV-1 p17 variants is driven by PAR1-mediated EGF transactivation.

Combined antiretroviral therapy (cART) for HIV-1 dramatically slows disease progression among HIV+ individuals. Currently, lymphoma represents the main cause of death among HIV-1-infected patients. Detection of p17 variants (vp17s) endowed with B-cell clonogenic activity in HIV-1-seropositive patients with lymphoma suggests their possible role in lymphomagenesis. Here, we demonstrate that the clonogenic activity of vp17s is mediated by their binding to PAR1 and to PAR1-mediated EGFR transactivation through Gq protein. The entire vp17s-triggered clonogenic process is MMPs dependent. Moreover, phosphoproteomic and bioinformatic analysis highlighted the crucial role of EGFR/PI3K/Akt pathway in modulating several molecules promoting cancer progression, including RAC1, ABL1, p53, CDK1, NPM, Rb, PTP-1B, and STAT1. Finally, we show that a peptide (F1) corresponding to the vp17s functional epitope is sufficient to trigger the PAR1/EGFR/PI3K/Akt pathway and bind PAR1. Our findings suggest novel potential therapeutic targets to counteract vp17-driven lymphomagenesis in HIV+ patients.