Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.

OBJECTIVE: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). METHODS: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. RESULTS: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters. INTERPRETATION: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials. ANN NEUROL 2024;95:1178-1192.

The Search for System's Parameters: Statistical and Dynamical Description from Complex Network Analysis.

The integration of physical and biological science styles is the key for facing the deluge of molecular level information that is becoming a real threat for knowledge advancement. In this work, I will indicate a possible integration path based on the network formalization of molecular knowledge by two different (here named flux and dynamical) perspectives. Some theoretical and applicative cases are presented, focusing on the different physical models implicit in the two network analysis approaches.

From Cell States to Cell Fates: Control of Cell State Transitions.

We examine the coordinated behavior of thousands of genes in cell fate transitions through genome expression as an integrated dynamical system using the concepts of self-organized criticality and coherent stochastic behavior. To quantify the effects of the collective behavior of genes, we adopted the flux balance approach and developed it in a new tool termed expression flux analysis (EFA). Here we describe this tool and demonstrate how its application to specific experimental genome-wide expression data provides new insights into the dynamics of the cell-fate transitions. Particularly, we show that in cell fate change, specific stochastic perturbations can spread over the entire system to guide distinct cell fate transitions through switching cyclic flux flow in the genome engine. Utilization of EFA enables us to elucidate a unified genomic mechanism for when and how cell-fate change occurs through critical transitions.

Increased Plasmatic Levels of Exosomes Are Significantly Related to Relapse Rate in Patients with Oral Squamous Cell Carcinoma: A Cohort Study.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is characterized by an immunosuppressive tumor microenvironment. Their plasma-derived exosomes deliver immunomodulatory molecules and cargo that correlate significantly with clinical parameters. This study aims to assess the exosomal profile as a potential tool for early detection of relapse and long-term outcomes in OSCC patients undergoing conventional therapy. METHODS: 27 OSCC patients with a median 38-month follow-up were included in this study. The relationship between NTA-derived parameters and clinical pathological parameters was examined, and receiver operating characteristic (ROC) curves were utilized to evaluate the diagnostic efficacy of these values in detecting cancer relapse. RESULTS: Plasmatic levels of exosomes prior to surgery showed a drastic reduction after surgical intervention (8.08E vs. 1.41 × 109 particles/mL, p = 0.006). Postsurgical concentrations of exosomes were higher in patients who experienced relapse compared to those who remained disease-free (2.97 × 109 vs. 1.11 × 109 particles/mL, p = 0.046). Additionally, patients who relapsed exhibited larger exosome sizes after surgery (141.47 vs. 132.31 nm, p = 0.03). Patients with lower concentrations of exosomes prior to surgery demonstrated better disease-free survival compared to those with higher levels (p = 0.012). ROC analysis revealed an area under the curve of 0.82 for presurgical exosome concentration in identifying relapse. CONCLUSIONS: Presurgical exosomal plasmatic levels serve as independent predictors of early recurrence and survival in OSCC. All in all, our findings indicate that the detection of peripheral exosomes represents a novel tool for the clinical management of OSCC, with potential implications for prognosis assessment.

Stem cell-derived small extracellular vesicles embedded into methacrylated hyaluronic acid wound dressings accelerate wound repair in a pressure model of diabetic ulcer.

Over the past years, the development of innovative smart wound dressings is revolutionizing wound care management and research. Specifically, in the treatment of diabetic foot wounds, three-dimensional (3D) bioprinted patches may enable personalized medicine therapies. In the present work, a methacrylated hyaluronic acid (MeHA) bioink is employed to manufacture 3D printed patches to deliver small extracellular vesicles (sEVs) obtained from human mesenchymal stem cells (MSC-sEVs). The production of sEVs is maximized culturing MSCs in bioreactor. A series of in vitro analyses are carried out to demonstrate the influence of MSC-sEVs on functions of dermal fibroblasts and endothelial cells, which are the primary functional cells in skin repair process. Results demonstrate that both cell populations are able to internalize MSC-sEVs and that the exposure to sEVs stimulates proliferation and migration. In vivo experiments in a well-established diabetic mouse model of pressure ulcer confirm the regenerative properties of MSC-sEVs. The MeHA patch enhances the effectiveness of sEVs by enabling controlled release of MSC-sEVs over 7 days, which improve wound epithelialization, angiogenesis and innervation. The overall findings highlight that MSC-sEVs loading in 3D printed biomaterials represents a powerful technique, which can improve the translational potential of parental stem cell in terms of regulatory and economic impact.

Special Issue "Advances in Genome Regulation in Cancer".

Cancer is globally increasing [...].

Wheel Running Adversely Affects Disease Onset and Neuromuscular Interplay in Amyotrophic Lateral Sclerosis Slow Progression Mouse Model.

BACKGROUND: Physical activity in Amyotrophic Lateral Sclerosis (ALS) plays a controversial role. In some epidemiological studies, both recreational or professional sport exercise has been associated to an increased risk for ALS but the mechanisms underlying the effects of exercise have not been fully elucidated in either patients or animal models. METHODS: To better reproduce the influence of this environmental factor in the pathogenesis of ALS, we exposed SOD1G93A low-copy male mice to multiple exercise sessions at asymptomatic and pre-symptomatic disease stages in an automated home-cage running-wheel system for about 3 months. RESULTS: Repeated voluntary running negatively influenced disease progression by anticipating disease onset, impairing neuromuscular transmission, worsening neuromuscular decline, and exacerbating muscle atrophy. Muscle fibers and neuromuscular junctions (NMJ) as well as key molecular players of the nerve-muscle circuit were similarly affected. CONCLUSION: It thus appears that excessive physical activity can be detrimental in predisposed individuals and these findings could model the increased risk of developing ALS in predisposed and specific professional athletes.

Is cancer an intelligent species?

Some relevant emerging properties of intelligent systems are "adaptation to a changing environment," "reaction to unexpected situations," "capacity of problem solving," and "ability to communicate." Single cells have remarkable abilities to adapt, make adequate context-dependent decision, take constructive actions, and communicate, thus theoretically meeting all the above-mentioned requirements. From a biological point of view, cancer can be viewed as an invasive species, composed of cells that move from primary to distant sites, being continuously exposed to changes in the environmental conditions. Blood represents the first hostile habitat that a cancer cell encounters once detached from the primary site, so that cancer cells must rapidly carry out multiple adaptation strategies to survive. The aim of this review was to deepen the adaptation mechanisms of cancer cells in the blood microenvironment, particularly referring to four adaptation strategies typical of animal species (phenotypic adaptation, metabolic adaptation, niche adaptation, and collective adaptation), which together define the broad concept of biological intelligence. We provided evidence that the required adaptations (either structural, metabolic, and related to metastatic niche formation) and "social" behavior are useful principles allowing putting into a coherent frame many features of circulating cancer cells. This interpretative frame is described by the comparison with analog behavioral traits typical of various animal models.

Systems Biology and Omics Approaches for Complex Human Diseases.

For many years, there has been general interest in developing virtual cells or digital twin models [...].

Synchronization between Attractors: Genomic Mechanism of Cell-Fate Change.

Herein, we provide a brief overview of complex systems theory approaches to investigate the genomic mechanism of cell-fate changes. Cell trajectories across the epigenetic landscape, whether in development, environmental responses, or disease progression, are controlled by extensively coordinated genome-wide gene expression changes. The elucidation of the mechanisms underlying these coherent expression changes is of fundamental importance in cell biology and for paving the road to new therapeutic approaches. In previous studies, we pointed at dynamic criticality as a plausible characteristic of genome-wide transition dynamics guiding cell fate. Whole-genome expression develops an engine-like organization (genome engine) in order to establish an autonomous dynamical system, capable of both homeostasis and transition behaviors. A critical set of genes behaves as a critical point (CP) that serves as the organizing center of cell-fate change. When the system is pushed away from homeostasis, the state change that occurs at the CP makes local perturbation spread over the genome, demonstrating self-organized critical (SOC) control of genome expression. Oscillating-Mode genes (which normally keep genome expression on pace with microenvironment fluctuations), when in the presence of an effective perturbative stimulus, drive the dynamics of synchronization, and thus guide the cell-fate transition.

Network-Based Prediction of Side Effects of Repurposed Antihypertensive Sartans against COVID-19 via Proteome and Drug-Target Interactomes.

The potential of targeting the Renin-Angiotensin-Aldosterone System (RAAS) as a treatment for the coronavirus disease 2019 (COVID-19) is currently under investigation. One way to combat this disease involves the repurposing of angiotensin receptor blockers (ARBs), which are antihypertensive drugs, because they bind to angiotensin-converting enzyme 2 (ACE2), which in turn interacts with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. However, there has been no in silico analysis of the potential toxicity risks associated with the use of these drugs for the treatment of COVID-19. To address this, a network-based bioinformatics methodology was used to investigate the potential side effects of known Food and Drug Administration (FDA)-approved antihypertensive drugs, Sartans. This involved identifying the human proteins targeted by these drugs, their first neighbors, and any drugs that bind to them using publicly available experimentally supported data, and subsequently constructing proteomes and protein-drug interactomes. This methodology was also applied to Pfizer's Paxlovid, an antiviral drug approved by the FDA for emergency use in mild-to-moderate COVID-19 treatment. The study compares the results for both drug categories and examines the potential for off-target effects, undesirable involvement in various biological processes and diseases, possible drug interactions, and the potential reduction in drug efficiency resulting from proteoform identification.

Globally invariant behavior of oncogenes and random genes at population but not at single cell level.

Cancer is widely considered a genetic disease. Notably, recent works have highlighted that every human gene may possibly be associated with cancer. Thus, the distinction between genes that drive oncogenesis and those that are associated to the disease, but do not play a role, requires attention. Here we investigated single cells and bulk (cell-population) datasets of several cancer transcriptomes and proteomes in relation to their healthy counterparts. When analyzed by machine learning and statistical approaches in bulk datasets, both general and cancer-specific oncogenes, as defined by the Cancer Genes Census, show invariant behavior to randomly selected gene sets of the same size for all cancers. However, when protein-protein interaction analyses were performed, the oncogenes-derived networks show higher connectivity than those relative to random genes. Moreover, at single-cell scale, we observe variant behavior in a subset of oncogenes for each considered cancer type. Moving forward, we concur that the role of oncogenes needs to be further scrutinized by adopting protein causality and higher-resolution single-cell analyses.

"Combo" Multi-Target Pharmacological Therapy and New Formulations to Reduce Inflammation and Improve Endogenous Remyelination in Traumatic Spinal Cord Injury.

Spinal cord injury (SCI) is characterized by a cascade of events that lead to sensory and motor disabilities. To date, this condition is irreversible, and no cure exists. To improve myelin repair and limit secondary degeneration, we developed a multitherapy based on nanomedicines (NMeds) loaded with the promyelinating agent triiodothyronine (T3), used in combination with systemic ibuprofen and mouse nerve growth factor (mNGF). Poly-L-lactic-co-glycolic acid (PLGA) NMeds were optimized and loaded with T3 to promote sustained release. In vitro experiments confirmed the efficacy of T3-NMeds to differentiate oligodendrocyte precursor cells. In vivo rat experiments were performed in contusion SCI to explore the NMed biodistribution and efficacy of combo drugs at short- and long-term post-lesion. A strong anti-inflammatory effect was observed in the short term with a reduction of type M1 microglia and glutamate levels, but with a subsequent increase of TREM2. In the long term, an improvement of myelination in NG2-IR, an increase in MBP content, and a reduction of the demyelination area were observed. These data demonstrated that NMeds can successfully be used to obtain more controlled local drug delivery and that this multiple treatment could be effective in improving the outcome of SCIs.

A novel network approach to multiscale biological regulation.

Modeling systems at multiple interacting scales is probably the most relevant task for pursuing a physically motivated explanation of biological regulation. In a new study, Smart and Zilman develop a convincing, albeit preliminary, model of the interplay between the cell microscale and the macroscopic tissue organization in biological systems.

Dormancy, stemness, and therapy resistance: interconnected players in cancer evolution.

The biological complexity of cancer represents a tremendous clinical challenge, resulting in the frequent failure of current treatment protocols. In the rapidly evolving scenario of a growing tumor, anticancer treatments impose a drastic perturbation not only to cancer cells but also to the tumor microenvironment, killing a portion of the cells and inducing a massive stress response in the survivors. Consequently, treatments can act as a double-edged sword by inducing a temporary response while laying the ground for therapy resistance and subsequent disease progression. Cancer cell dormancy (or quiescence) is a central theme in tumor evolution, being tightly linked to the tumor's ability to survive cytotoxic challenges, metastasize, and resist immune-mediated attack. Accordingly, quiescent cancer cells (QCCs) have been detected in virtually all the stages of tumor development. In recent years, an increasing number of studies have focused on the characterization of quiescent/therapy resistant cancer cells, unveiling QCCs core transcriptional programs, metabolic plasticity, and mechanisms of immune escape. At the same time, our partial understanding of tumor quiescence reflects the difficulty to identify stable QCCs biomarkers/therapeutic targets and to control cancer dormancy in clinical settings. This review focuses on recent discoveries in the interrelated fields of dormancy, stemness, and therapy resistance, discussing experimental evidences in the frame of a nonlinear dynamics approach, and exploring the possibility that tumor quiescence may represent not only a peril but also a potential therapeutic resource.

Spatial-Temporal Genome Regulation in Stress-Response and Cell-Fate Change.

Complex functioning of the genome in the cell nucleus is controlled at different levels: (a) the DNA base sequence containing all relevant inherited information; (b) epigenetic pathways consisting of protein interactions and feedback loops; (c) the genome architecture and organization activating or suppressing genetic interactions between different parts of the genome. Most research so far has shed light on the puzzle pieces at these levels. This article, however, attempts an integrative approach to genome expression regulation incorporating these different layers. Under environmental stress or during cell development, differentiation towards specialized cell types, or to dysfunctional tumor, the cell nucleus seems to react as a whole through coordinated changes at all levels of control. This implies the need for a framework in which biological, chemical, and physical manifestations can serve as a basis for a coherent theory of gene self-organization. An international symposium held at the Biomedical Research and Study Center in Riga, Latvia, on 25 July 2022 addressed novel aspects of the abovementioned topic. The present article reviews the most recent results and conclusions of the state-of-the-art research in this multidisciplinary field of science, which were delivered and discussed by scholars at the Riga symposium.

Computational analysis of the sequence-structure relation in SARS-CoV-2 spike protein using protein contact networks.

The structure of proteins impacts directly on the function they perform. Mutations in the primary sequence can provoke structural changes with consequent modification of functional properties. SARS-CoV-2 proteins have been extensively studied during the pandemic. This wide dataset, related to sequence and structure, has enabled joint sequence-structure analysis. In this work, we focus on the SARS-CoV-2 S (Spike) protein and the relations between sequence mutations and structure variations, in order to shed light on the structural changes stemming from the position of mutated amino acid residues in three different SARS-CoV-2 strains. We propose the use of protein contact network (PCN) formalism to: (i) obtain a global metric space and compare various molecular entities, (ii) give a structural explanation of the observed phenotype, and (iii) provide context dependent descriptors of single mutations. PCNs have been used to compare sequence and structure of the Alpha, Delta, and Omicron SARS-CoV-2 variants, and we found that omicron has a unique mutational pattern leading to different structural consequences from mutations of other strains. The non-random distribution of changes in network centrality along the chain has allowed to shed light on the structural (and functional) consequences of mutations.

Poly(l-lactic acid) Scaffold Releasing an α4ß1 Integrin Agonist Promotes Nonfibrotic Skin Wound Healing in Diabetic Mice.

Skin wound healing is a highly complex process that continues to represent a major medical problem, due to chronic nonhealing wounds in several classes of patients and to possible fibrotic complications, which compromise the function of the dermis. Integrins are transmembrane receptors that play key roles in this process and that offer a recognized druggable target. Our group recently synthesized GM18, a specific agonist for α4ß1, an integrin that plays a role in skin immunity and in the migration of neutrophils, also regulating the differentiated state of fibroblasts. GM18 can be combined with poly(l-lactic acid) (PLLA) nanofibers to provide a controlled release of this agonist, resulting in a medication particularly suitable for skin wounds. In this study, we first optimized a GM18-PLLA nanofiber combination with a 7-day sustained release for use as skin wound medication. When tested in an experimental pressure ulcer in diabetic mice, a model for chronic nonhealing wounds, both soluble and GM18-PLLA formulations accelerated wound healing, as well as regulated extracellular matrix synthesis toward a nonfibrotic molecular signature. In vitro experiments using the adhesion test showed fibroblasts to be a principal GM18 cellular target, which we then used as an in vitro model to explore possible mechanisms of GM18 action. Our results suggest that the observed antifibrotic behavior of GM18 may exert a dual action on fibroblasts at the α4ß1 binding site and that GM18 may prevent profibrotic EDA-fibronectin-α4ß1 binding and activate outside-in signaling of the ERK1/2 pathways, a critical component of the wound healing process.

Brain metabolic differences between pure bulbar and pure spinal ALS: a 2-[18F]FDG-PET study.

BACKGROUND: MRI studies reported that ALS patients with bulbar and spinal onset showed focal cortical changes in corresponding regions of the motor homunculus. We evaluated the capability of brain 2-[18F]FDG-PET to disclose the metabolic features characterizing patients with pure bulbar or spinal motor impairment. METHODS: We classified as pure bulbar (PB) patients with bulbar onset and a normal score in the spinal items of the ALSFRS-R, and as pure spinal (PS) patients with spinal onset and a normal score in the bulbar items at the time of PET. Forty healthy controls (HC) were enrolled. We compared PB and PS, and each patient group with HC. Metabolic clusters showing a statistically significant difference between PB and PS were tested to evaluate their accuracy in discriminating the two groups. We performed a leave-one-out cross-validation (LOOCV) over the entire dataset. Four classifiers were considered: support vector machines (SVM), K-nearest neighbours, linear classifier, and decision tree. Then, we used a separate test set, including 10% of patients, with the remaining 90% composing the training set. RESULTS: We included 63 PB, 271 PS, and 40 HC. PB showed a relative hypometabolism compared to PS in bilateral precentral gyrus in the regions of the motor cortex involved in the control of bulbar function. SVM showed the best performance, resulting in the lowest error rate in both LOOCV (4.19%) and test set (9.09 ± 2.02%). CONCLUSIONS: Our data support the concept of the focality of ALS onset and the use of 2-[18F]FDG-PET as a biomarker for precision medicine-oriented clinical trials.

Role of brain 2-[18F]fluoro-2-deoxy-D-glucose-positron-emission tomography as survival predictor in amyotrophic lateral sclerosis.

PURPOSE: The identification of prognostic tools in amyotrophic lateral sclerosis (ALS) would improve the design of clinical trials, the management of patients, and life planning. We aimed to evaluate the accuracy of brain 2-[18F]fluoro-2-deoxy-D-glucose-positron-emission tomography (2-[18F]FDG-PET) as an independent predictor of survival in ALS. METHODS: A prospective cohort study enrolled 418 ALS patients, who underwent brain 2-[18F]FDG-PET at diagnosis and whose survival time was available. We discretized the survival time in a finite number of classes in a data-driven fashion by employing a k-means-like strategy. We identified "hot brain regions" with maximal power in discriminating survival classes, by evaluating the Laplacian scores in a class-aware fashion. We retained the top-m features for each class to train the classification systems (i.e., a support vector machine, SVM), using 10% of the ALS cohort as test set. RESULTS: Data were discretized in three survival profiles: 0-2 years, 2-5 years, and > 5 years. SVM resulted in an error rate < 20% for two out of three classes separately. As for class one, the discriminant clusters included left caudate body and anterior cingulate cortex. The most discriminant regions were bilateral cerebellar pyramid in class two, and right cerebellar dentate nucleus, and left cerebellar nodule in class three. CONCLUSION: Brain 2-[18F]FDG-PET along with artificial intelligence was able to predict with high accuracy the survival time range in our ALS cohort. Healthcare professionals can benefit from this prognostic tool for planning patients' management and follow-up. 2-[18F]FDG-PET represents a promising biomarker for individual patients' stratification in clinical trials. The lack of a multicentre external validation of the model warrants further studies to evaluate its generalization capability.