Molecular Origins of the Mendelian Rare Diseases Reviewed by Orpha.net: A Structural Bioinformatics Investigation.

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the pathogenic missense variants found in the 2515 proteins involved in Mendelian rare diseases (MRDs), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRDs showed different mutation site distributions at different three-dimensional protein depths. We then highlighted the depth-dependent effects of pathogenic variants for the 20,061 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

Alkaptonuria: From Molecular Insights to a Dedicated Digital Platform.

Alkaptonuria (AKU) is a genetic disorder that affects connective tissues of several body compartments causing cartilage degeneration, tendon calcification, heart problems, and an invalidating, early-onset form of osteoarthritis. The molecular mechanisms underlying AKU involve homogentisic acid (HGA) accumulation in cells and tissues. HGA is highly reactive, able to modify several macromolecules, and activates different pathways, mostly involved in the onset and propagation of oxidative stress and inflammation, with consequences spreading from the microscopic to the macroscopic level leading to irreversible damage. Gaining a deeper understanding of AKU molecular mechanisms may provide novel possible therapeutical approaches to counteract disease progression. In this review, we first describe inflammation and oxidative stress in AKU and discuss similarities with other more common disorders. Then, we focus on HGA reactivity and AKU molecular mechanisms. We finally describe a multi-purpose digital platform, named ApreciseKUre, created to facilitate data collection, integration, and analysis of AKU-related data.

Repurposing Castanea sativa Spiny Burr By-Products Extract as a Potentially Effective Anti-Inflammatory Agent for Novel Future Biotechnological Applications.

The concept of a "circular bioeconomy" holds great promise for the health, cosmetic, and nutrition sectors by re-using Castanea sativa (Mill.) by-products. This sustainable resource is rich in bioactive secondary metabolites with antioxidant and anti-inflammatory properties. By transforming these by-products into high-value products for human health, we can promote sustainable economic growth and reduce the environmental impact of traditional waste disposal, adding value to previously underutilized resources. In the present study, we investigated the antioxidant capacity, phytochemical composition, and in vitro antioxidant and anti-inflammatory activity of C. sativa burr (CSB) aqueous extract. The spectrophotometric study revealed high total phenolic content (TPC) values with significant antioxidant and anti-radical properties. Using UPLC-MS/MS techniques, the phytochemical investigation identified 56 metabolites, confirming the presence of phenolic compounds in CSBs. In addition, CSBs significantly downregulated pro-inflammatory mediators in LPS-stimulated RAW 264.7 macrophage cells without significant cell toxicity. Lastly, in silico studies pinpointed three kinases from RAW 264.7 cells as binding partners with ellagic acid, the predominant compound found in our extract. These findings strongly advocate for the recycling and valorization of C. sativa by-products, challenging their conventional classification as mere "waste".

Molecular and Evolution In Silico Studies Unlock the h4-HPPD C-Terminal Tail Gating Mechanism.

The enzyme 4-hydroxyphenylpyruvate dioxygenase (4-HPPD) is involved in the catabolism of the amino acid tyrosine in organisms such as bacteria, plants, and animals. It catalyzes the conversion of 4-hydroxyphenylpyruvate to a homogenisate in the presence of molecular oxygen and Fe(II) as a cofactor. This enzyme represents a key step in the biosynthesis of important compounds, and its activity deficiency leads to severe, rare autosomal recessive disorders, like tyrosinemia type III and hawkinsinuria, for which no cure is currently available. The 4-HPPD C-terminal tail plays a crucial role in the enzyme catalysis/gating mechanism, ensuring the integrity of the active site for catalysis through fine regulation of the C-terminal tail conformation. However, despite growing interest in the 4-HPPD catalytic mechanism and structure, the gating mechanism remains unclear. Furthermore, the absence of the whole 3D structure makes the bioinformatic approach the only possible study to define the enzyme structure/molecular mechanism. Here, wild-type 4-HPPD and its mutants were deeply dissected by applying a comprehensive bioinformatics/evolution study, and we showed for the first time the entire molecular mechanism and regulation of the enzyme gating process, proposing the full-length 3D structure of human 4-HPPD and two novel key residues involved in the 4-HPPD C-terminal tail conformational change.

Phytochemical Composition, Anti-Inflammatory Property, and Anti-Atopic Effect of Chaetomorpha linum Extract.

Utilizing plant-based resources, particularly their by-products, aligns with sustainability principles and circular bioeconomy, contributing to environmental preservation. The therapeutic potential of plant extracts is garnering increasing interest, and this study aimed to demonstrate promising outcomes from an extract obtained from an underutilized plant waste. Chaetomorpha linum, an invasive macroalga found in the Orbetello Lagoon, thrives in eutrophic conditions, forming persistent mats covering approximately 400 hectares since 2005. The biomass of C. linum undergoes mechanical harvesting and is treated as waste, requiring significant human efforts and economic resources-A critical concern for municipalities. Despite posing challenges to local ecosystems, the study identified C. linum as a natural source of bioactive metabolites. Phytochemical characterization revealed lipids, amino acids, and other compounds with potential anti-inflammatory activity in C. linum extract. In vitro assays with LPS-stimulated RAW 264.7 and TNF-α/IFN-γ-stimulated HaCaT cells showed the extract inhibited reactive oxygen species (ROS), nitric oxide (NO), and prostaglandin E2 (PGE2) productions, and reduced inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) expressions via NF-κB nuclear translocation, in RAW 264.7 cells. It also reduced chemokines (TARC/CCL17, RANTES/CCL5, MCP-1/CCL2, and IL-8) and the cytokine IL-1ß production in HaCaT cells, suggesting potential as a therapeutic candidate for chronic diseases like atopic dermatitis. Finally, in silico studies indicated palmitic acid as a significant contributor to the observed effect. This research not only uncovered the untapped potential of C. linum but also laid the foundation for its integration into the circular bioeconomy, promoting sustainable practices, and innovative applications across various industries.

Alkaptonuria.

Alkaptonuria is a rare inborn error of metabolism caused by the deficiency of homogentisate 1,2-dioxygenase activity. The consequent homogentisic acid (HGA) accumulation in body fluids and tissues leads to a multisystemic and highly debilitating disease whose main features are dark urine, ochronosis (HGA-derived pigment in collagen-rich connective tissues), and a painful and severe form of osteoarthropathy. Other clinical manifestations are extremely variable and include kidney and prostate stones, aortic stenosis, bone fractures, and tendon, ligament and/or muscle ruptures. As an autosomal recessive disorder, alkaptonuria affects men and women equally. Debilitating symptoms appear around the third decade of life, but a proper and timely diagnosis is often delayed due to their non-specific nature and a lack of knowledge among physicians. In later stages, patients' quality of life might be seriously compromised and further complicated by comorbidities. Thus, appropriate management of alkaptonuria requires a multidisciplinary approach, and periodic clinical evaluation is advised to monitor disease progression, complications and/or comorbidities, and to enable prompt intervention. Treatment options are patient-tailored and include a combination of medications, physical therapy and surgery. Current basic and clinical research focuses on improving patient management and developing innovative therapies and implementing precision medicine strategies.

A Drug Discovery Approach to a Reveal Novel Antioxidant Natural Source: The Case of Chestnut Burr Biomass.

Currently, many environmental and energy-related problems are threatening the future of our planet. In October 2022, the Worldmeter recorded the world population as 7.9 billion people, estimating that there will be an increase of 2 billion by 2057. The rapid growth of the population and the continuous increase in needs are causing worrying conditions, such as pollution, climate change, global warming, waste disposal, and natural resource reduction. Looking for novel and innovative methods to overcome these global troubles is a must for our common welfare. The circular bioeconomy represents a promising strategy to alleviate the current conditions using biomass-like natural wastes to replace commercial products that have a negative effect on our ecological footprint. Applying the circular bioeconomy concept, we propose an integrated in silico and in vitro approach to identify antioxidant bioactive compounds extracted from chestnut burrs (an agroforest waste) and their potential biological targets. Our study provides a novel and robust strategy developed within the circular bioeconomy concept aimed at target and drug discovery for a wide range of diseases. Our study could open new frontiers in the circular bioeconomy related to target and drug discovery, offering new ideas for sustainable scientific research aimed at identifying novel therapeutical strategies.

Recent Advances in Organic Dyes for Application in Dye-Sensitized Solar Cells under Indoor Lighting Conditions.

Among the emerging photovoltaic (PV) technologies, Dye-Sensitized Solar Cells (DSSCs) appear especially interesting in view of their potential for unconventional PV applications. In particular, DSSCs have been proven to provide excellent performances under indoor illumination, opening the way to their use in the field of low-power devices, such as wearable electronics and wireless sensor networks, including those relevant for application to the rapidly growing Internet of Things technology. Considering the low intensity of indoor light sources, efficient light capture constitutes a pivotal factor in optimizing cell efficiency. Consequently, the development of novel dyes exhibiting intense absorption within the visible range and light-harvesting properties well-matched with the emission spectra of the various light sources becomes indispensable. In this review, we will discuss the current state-of-the-art in the design, synthesis, and application of organic dyes as sensitizers for indoor DSSCs, focusing on the most recent results. We will start by examining the various classes of individual dyes reported to date for this application, organized by their structural features, highlighting their strengths and weaknesses. On the basis of this discussion, we will then draft some potential guidelines in an effort to help the design of this kind of sensitizer. Subsequently, we will describe some alternative approaches investigated to improve the light-harvesting properties of the cells, such as the co-sensitization strategy and the use of concerted companion dyes. Finally, the issue of measurement standardization will be introduced, and some considerations regarding the proper characterization methods of indoor PV systems and their differences compared to (simulated) outdoor conditions will be provided.

Supporting Machine Learning Model in the Treatment of Chronic Pain.

Conventional therapy options for chronic pain are still insufficient and patients most frequently request alternative medical treatments, such as medical cannabis. Although clinical evidence supports the use of cannabis for pain, very little is known about the efficacy, dosage, administration methods, or side effects of widely used and accessible cannabis products. A possible solution could be given by pharmacogenetics, with the identification of several polymorphic genes that may play a role in the pharmacodynamics and pharmacokinetics of cannabis. Based on these findings, data from patients treated with cannabis and genotyped for several candidate polymorphic genes (single-nucleotide polymorphism: SNP) were collected, integrated, and analyzed through a machine learning (ML) model to demonstrate that the reduction in pain intensity is closely related to gene polymorphisms. Starting from the patient's data collected, the method supports the therapeutic process, avoiding ineffective results or the occurrence of side effects. Our findings suggest that ML prediction has the potential to positively influence clinical pharmacogenomics and facilitate the translation of a patient's genomic profile into useful therapeutic knowledge.

Structure-Function Relationship of Homogentisate 1,2-dioxygenase: Understanding the Genotype-Phenotype Correlations in the Rare Genetic Disease Alkaptonuria.

Alkaptonuria (AKU), a rare genetic disorder, is characterized by the accumulation of homogentisic acid (HGA) in organs, which occurs because the homogentisate 1,2-dioxygenase (HGD) enzyme is not functional due to gene variants. Over time, HGA oxidation and accumulation cause the formation of the ochronotic pigment, a deposit that provokes tissue degeneration and organ malfunction. Here, we report a comprehensive review of the variants so far reported, the structural studies on the molecular consequences of protein stability and interaction, and molecular simulations for pharmacological chaperones as protein rescuers. Moreover, evidence accumulated so far in alkaptonuria research will be re-proposed as the bases for a precision medicine approach in a rare disease.

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology.

Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.

Dimerization of the C-type lectin-like receptor CD93 promotes its binding to Multimerin-2 in endothelial cells.

Blocking the signaling activated by the plasma membrane receptor CD93 has recently been demonstrated a useful tool in antiangiogenic treatment and oncotherapy. In the proliferating endothelium, CD93 regulates cell adhesion, migration, and vascular maturation, yet it is unclear how CD93 interacts with the extracellular matrix activating signaling pathways involved in the vascular remodeling. Here for the first time we show that in endothelial cells CD93 is structured as a dimer and that this oligomeric form is physiologically instrumental for the binding of CD93 to its ligand Multimerin-2. Crystallographic X-ray analysis of recombinant CD93 reveals the crucial role played by the C-type lectin-like and sushi-like domains in arranging as an antiparallel dimer to achieve a functional binding state, providing key information for the future design of new drugs able to hamper CD93 function in neovascular pathologies.

Untargeted NMR Metabolomics Reveals Alternative Biomarkers and Pathways in Alkaptonuria.

Alkaptonuria (AKU) is an ultra-rare metabolic disease caused by the accumulation of homogentisic acid (HGA), an intermediate product of phenylalanine and tyrosine degradation. AKU patients carry variants within the gene coding for homogentisate-1,2-dioxygenase (HGD), which are responsible for reducing the enzyme catalytic activity and the consequent accumulation of HGA and formation of a dark pigment called the ochronotic pigment. In individuals with alkaptonuria, ochronotic pigmentation of connective tissues occurs, leading to inflammation, degeneration, and eventually osteoarthritis. The molecular mechanisms underlying the multisystemic development of the disease severity are still not fully understood and are mostly limited to the metabolic pathway segment involving HGA. In this view, untargeted metabolomics of biofluids in metabolic diseases allows the direct investigation of molecular species involved in pathways alterations and their interplay. Here, we present the untargeted metabolomics study of AKU through the nuclear magnetic resonance of urine from a cohort of Italian patients; the study aims to unravel molecular species and mechanisms underlying the AKU metabolic disorder. Dysregulation of metabolic pathways other than the HGD route and new potential biomarkers beyond homogentisate are suggested, contributing to a more comprehensive molecular signature definition for AKU and the development of future adjuvant treatment.

Effects of Nitisinone on Oxidative and Inflammatory Markers in Alkaptonuria: Results from SONIA1 and SONIA2 Studies.

Nitisinone (NTBC) was recently approved to treat alkaptonuria (AKU), but there is no information on its impact on oxidative stress and inflammation, which are observed in AKU. Therefore, serum samples collected during the clinical studies SONIA1 (40 AKU patients) and SONIA2 (138 AKU patients) were tested for Serum Amyloid A (SAA), CRP and IL-8 by ELISA; Advanced Oxidation Protein Products (AOPP) by spectrophotometry; and protein carbonyls by Western blot. Our results show that NTBC had no significant effects on the tested markers except for a slight but statistically significant effect for NTBC, but not for the combination of time and NTBC, on SAA levels in SONIA2 patients. Notably, the majority of SONIA2 patients presented with SAA > 10 mg/L, and 30 patients in the control group (43.5%) and 40 patients (58.0%) in the NTBC-treated group showed persistently elevated SAA > 10 mg/L at each visit during SONIA2. Higher serum SAA correlated with lower quality of life and higher morbidity. Despite no quantitative differences in AOPP, the preliminary analysis of protein carbonyls highlighted patterns that deserve further investigation. Overall, our results suggest that NTBC cannot control the sub-clinical inflammation due to increased SAA observed in AKU, which is also a risk factor for developing secondary amyloidosis.

Bioinformatics Approaches to Predict Mutation Effects in the Binding Site of the Proangiogenic Molecule CD93.

The transmembrane glycoprotein CD93 has been identified as a potential new target to inhibit tumor angiogenesis. Recently, Multimerin-2 (MMRN2), a pan-endothelial extracellular matrix protein, has been identified as a ligand for CD93, but the interaction mechanism between these two proteins is yet to be studied. In this article, we aim to investigate the structural and functional effects of induced mutations on the binding domain of CD93 to MMRN2. Starting from experimental data, we assessed how specific mutations in the C-type lectin-like domain (CTLD) affect the binding interaction profile. We described a four-step workflow in order to predict the effects of variations on the inter-residue interaction network at the PPI, based on evolutionary information, complex network metrics, and energetic affinity. We showed that the application of computational approaches, combined with experimental data, allowed us to gain more in-depth molecular insights into the CD93-MMRN2 interaction, offering a platform for developing innovative therapeutics able to target these molecules and block their interaction. This comprehensive molecular insight might prove useful in drug design in cancer therapy.

Evaluation of targeted oxidative stress induced by oxygen-ozone in vitro after ischemic induction.

Encephalic vascular accident, or stroke, is the most common pathology of the central nervous system in humans, the second leading cause of death and physical and cognitive disabilities, in developing countries. It presents as an ischemic (more common) or hemorrhagic form. Ozone therapy has been shown to be effective in neuromodulation, neuroprotection, and nerve regeneration. The present study aimed to evaluate the effect of targeted mild ozone after inducing cerebral ischemia in vitro. Neuroblastoma lineage cells (SH-SY5Y) and canine amniotic membrane stem cells were subjected to 24 hours of hypoxia in an incubator culture chamber. The cells were evaluated by MTT assay, colorimetric assay spectrophotometry, fluorescence microscopy, and flow cytometry. Treatment with low concentrations of ozone (2-10 µg/mL), indicated a possible neuroregenerative effect at low concentrations, correlated with lower levels of apoptosis and oxidative stress compared to cells not subjected to hypoxia. High concentrations of ozone (18-30 µg/mL) promoted an increase in rate of apoptosis and cell death. We developed a novel protocol that mimics ozone therapy for ischemic stroke, using ozonized culture medium after hypoxia induction. Although more studies are needed, we conclude that ozone has a dose-dependent hormetic effect and can reverse the effect of ischemia in vitro at low concentrations.

Artificial Intelligence Approaches in Drug Discovery: Towards the Laboratory of the Future.

The role of computational tools in the drug discovery and development process is becoming central, thanks to the possibility to analyze large amounts of data. The high throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets, has exponentially increased the volume of scientific data available. The quality of the data and the speed with which in silico predictions can be validated in vitro is instrumental in accelerating clinical laboratory medicine, significantly and substantially impacting Precision Medicine (PM). PM affords the basis to develop new drugs by providing a wide knowledge of the patient as an essential step towards individualized medicine. It is, therefore, essential to collect as much information and data as possible on each patient to identify the causes of the different responses to drugs from a pharmacogenomics perspective and to identify biological biomarkers capable of accurately describing the risk signals to develop specific diseases. Furthermore, the role of biomarkers in early drug discovery is increasing, as they can significantly reduce the time it takes to develop new drugs. This review article will discuss how Artificial Intelligence fits in the drug discovery pipeline, covering the benefits of an automated, integrated laboratory framework where the application of Machine Learning methodologies to interpret omics-based data can avail the future perspective of Translational Precision Medicine.

The Pharmacogenetics of Cannabis in the Treatment of Chronic Pain.

BACKGROUND: The increase in the medical use of cannabis has revealed a number of beneficial effects, a variety of adverse side effects and great inter-individual variability. Association studies connecting consumption, addiction and side effects related to recreational cannabis use have led to the identification of several polymorphic genes that may play a role in the pharmacodynamics and pharmacokinetics of cannabis. METHOD: In total, 600 patients treated with cannabis were genotyped for several candidate polymorphic genes (single-nucleotide polymorphism; SNP), encoding receptors CNR1 and TRPV1; for the ABCB1 transporter; for biotransformation, bioactivation and biosynthesis; and CYP3A4, COMT and UGT2B7 conjugation. RESULTS: Three polymorphic genes (ABCB1, TRPV1 and UGT2B7) were identified as being significantly associated with decline in pain after treatment with cannabis. Patients simultaneously carrying the most favourable allele combinations showed a greater reduction (polygenic effect) in pain compared to those with a less favourable combination. Considering genotype combinations, we could group patients into good responders, intermediate responders and poor or non-responders. Results suggest that genetic makeup is, at the moment, a significant predictive factor of the variability in response to cannabis. CONCLUSIONS: This study proves, for the first time, that certain polymorphic candidate genes may be associated with cannabis effects, both in terms of pain management and side effects, including therapy dropout. SIGNIFICANCE: Our attention to pharmacogenetics began in 2008, with the publication of a first study on the association between genetic polymorphisms and morphine action in pain relief. The study we are presenting is the first observational study conducted on a large number of patients involving several polymorphic candidate genes. The data obtained suggest that genetic makeup can be a predictive factor in the response to cannabis therapy and that more extensive and planned studies are needed for the opening of new scenarios for the personalization of cannabis therapy.

HGDiscovery: An online tool providing functional and phenotypic information on novel variants of homogentisate 1,2- dioxigenase.

Alkaptonuria (AKU), a rare genetic disorder, is characterized by the accumulation of homogentisic acid (HGA) in the body. Affected individuals lack functional levels of an enzyme required to breakdown HGA. Mutations in the homogentisate 1,2-dioxygenase (HGD) gene cause AKU and they are responsible for deficient levels of functional HGD, which, in turn, leads to excess levels of HGA. Although HGA is rapidly cleared from the body by the kidneys, in the long term it starts accumulating in various tissues, especially cartilage. Over time (rarely before adulthood), it eventually changes the color of affected tissue to slate blue or black. Here we report a comprehensive mutation analysis of 111 pathogenic and 190 non-pathogenic HGD missense mutations using protein structural information. Using our comprehensive suite of graph-based signature methods, mCSM complemented with sequence-based tools, we studied the functional and molecular consequences of each mutation on protein stability, interaction and evolutionary conservation. The scores generated from the structure and sequence-based tools were used to train a supervised machine learning algorithm with 89% accuracy. The empirical classifier was used to generate the variant phenotype for novel HGD missense mutations. All this information is deployed as a user friendly freely available web server called HGDiscovery (https://biosig.lab.uq.edu.au/hgdiscovery/).