MAPK15 controls cellular responses to oxidative stress by regulating NRF2 activity and expression of its downstream target genes.

Oxidation processes in mitochondria and different environmental insults contribute to unwarranted accumulation of reactive oxygen species (ROS). These, in turn, rapidly damage intracellular lipids, proteins, and DNA, ultimately causing aging and several human diseases. Cells have developed different and very effective systems to control ROS levels. Among these, removal of excessive amounts is guaranteed by upregulated expression of various antioxidant enzymes, through activation of the NF-E2-Related Factor 2 (NRF2) protein. Here, we show that Mitogen Activated Protein Kinase 15 (MAPK15) controls the transactivating potential of NRF2 and, in turn, the expression of its downstream target genes. Specifically, upon oxidative stress, MAPK15 is necessary to increase NRF2 expression and nuclear translocation, by inducing its activating phosphorylation, ultimately supporting transactivation of cytoprotective antioxidant genes. Lungs are continuously exposed to oxidative damages induced by environmental insults such as air pollutants and cigarette smoke. Interestingly, we demonstrate that MAPK15 is very effective in supporting NRF2-dependent antioxidant transcriptional response to cigarette smoke of epithelial lung cells. Oxidative damage induced by cigarette smoke indeed represents a leading cause of disability and death worldwide by contributing to the pathogenesis of different chronic respiratory diseases and lung cancer. Therefore, the development of novel therapeutic strategies able to modulate cellular responses to oxidative stress would be highly beneficial. Our data contribute to the necessary understanding of the molecular mechanisms behind such responses and identify new potentially actionable targets.

New orphan disease therapies from the proteome of industrial plasma processing waste- a treatment for aceruloplasminemia.

Plasma-derived therapeutic proteins are produced through an industrial fractionation process where proteins are purified from individual intermediates, some of which remain unused and are discarded. Relatively few plasma-derived proteins are exploited clinically, with most of available plasma being directed towards the manufacture of immunoglobulin and albumin. Although the plasma proteome provides opportunities to develop novel protein replacement therapies, particularly for rare diseases, the high cost of plasma together with small patient populations impact negatively on the development of plasma-derived orphan drugs. Enabling therapeutics development from unused plasma fractionation intermediates would therefore constitute a substantial innovation. To this objective, we characterized the proteome of unused plasma fractionation intermediates and prioritized proteins for their potential as new candidate therapies for human disease. We selected ceruloplasmin, a plasma ferroxidase, as a potential therapy for aceruloplasminemia, an adult-onset ultra-rare neurological disease caused by iron accumulation as a result of ceruloplasmin mutations. Intraperitoneally administered ceruloplasmin, purified from an unused plasma fractionation intermediate, was able to prevent neurological, hepatic and hematological phenotypes in ceruloplasmin-deficient mice. These data demonstrate the feasibility of transforming industrial waste plasma fraction into a raw material for manufacturing of new candidate proteins for replacement therapies, optimizing plasma use and reducing waste generation.

A proteomic approach to investigate the role of the MECP2 gene mutation in Rett syndrome redox regulatory pathways.

Mutations in the X-linked methyl-CpG-binding 2 (MECP2) gene lead to Rett Syndrome (RTT; OMIM 312750), a devasting neurodevelopmental disorder. RTT clinical manifestations are complex and with different degrees of severity, going from autistic-like behavior to loss of acquired speech, motor skills and cardiac problems. Furthermore, the correlation between the type of MECP2 mutation and the clinical phenotype is still not fully understood. Contextually, different genotypes can differently affect the patient's phenotype and omics methodologies such as proteomics could be an important tool for a molecular characterization of genotype/phenotype correlation. The aim of our study was focused on evaluating RTT oxidative stress (OS) responses related to specific MECP2 gene mutations by using proteomics and bioinformatics approaches. Primary fibroblasts isolated from patients affected by R133C and R255× mutations were compared to healthy controls (HC). After clustering primary dermal fibroblasts based on their specific MECP2 mutations, fibroblast-derived protein samples were qualitative and quantitative analyzed, using a label free quantification (LFQ) analysis by mass spectrometry (MS), achieving a preliminary correlation for RTT genotype/phenotype. Among the identified proteins involved in redox regulation pathways, NAD(P)H:quinone acceptor oxidoreductase 1 (NQO1) was found to be absent in R255× cells, while it was present in R133C and in HC fibroblasts. Moreover, NQO1 aberrant gene regulation was also confirmed when cells were challenged with 100 µM hydrogen peroxide (H2O2). In conclusion, by employing a multidisciplinary approach encompassing proteomics and bioinformatics analyses, as well as molecular biology assays, the study uncovered phenotypic responses linked to specific MECP2 gene mutations. These findings contribute to a better understanding of the complexity of RTT molecular pathways, confirming the high heterogeneity among the patients.

Hydroxyanthracene derivates citotoxicity: A differential evaluation between single molecule and whole plant extract.

Hydroxyanthracene derivates (HADs) are a group of natural or synthetic compounds with a wide range of biological activities (for instance, anti-inflammatory, antibacterial, and antiarthritic). In addition, because of their properties for helping the normal bowel function, HADs are widely used in constipation as pharmacological drugs and nutritional supplements. Nevertheless, during the past years, a safety usage of HAD products has been under consideration because some studies reported that HADs are not lacking toxicity (i.e., genotoxic and carcinogenic activity). Thus, the first objective of this study is to shed light on the large variability in composition of botanical food supplements containing HAD by a systematic analysis of the qualitative and quantitative composition of a cohort of extracts and raw materials of plants with high levels of anthraquinones commercially available (Cassia angustifolia, Rhamnus purshiana, Rhamnus frangula, Rheum palmatum, and Rheum raponticum). To date, the investigation of HAD toxicity was based on in vitro and in vivo studies conducted mainly on the use of the single molecules (emodin, aloe-emodin, and rhein) rather than on the whole plant extract. The qualitative-quantitative characterization was the starting point to select the most appropriate products to be used as treatment for our in vitro cell studies. Thus, the second objective of this study is the investigation, for the first time, of the toxic events of HAD used as single molecule in comparison with the whole plant extracts containing HAD in an intestinal in vitro model using human colorectal adenocarcinoma cells (Caco-2). In addition, a shotgun proteomics approach was applied to profile the differential protein expression in the Caco-2 cells after a single-HAD or whole-plant extract treatment to fully understand the potential targets and signaling pathways. In conclusion, the combination of a detailed phytochemical characterization of HAD products and a largely accurate analysis of the proteomic profile of intestinal cells treated with HAD products provided the opportunity to investigate their effects in the intestinal system.

SPME-GC-MS and PTR-ToF-MS Techniques for the Profiling of the Metabolomic Pattern of VOCs and GC-MS for the Determination of the Cannabinoid Content of Three Cultivars of Cannabis sativa L. Pollen.

Considering the large number of volatile molecules that characterize Cannabis sativa L., adequate investigation supported by the application of robust and effective analytical methods is essential to better understand the impact of these low- and medium-molecular-weight molecules on the entire phytocomplex. This work aimed to characterize the volatile fraction of the chemical profile of three different cultivars of Cannabis sativa L. pollen, grown in Italy, which were thoroughly investigated by the application of two complementary techniques: SPME-GC-MS and PTR-ToF-MS. Furthermore, in order to provide more information on the chemical profile of the matrices under study, the cannabinoid content of the hexane extracts was also measured by GC-MS. Until now, no similar study, in terms of survey techniques applied, has been performed on C. sativa pollen. The obtained results showed a high content of volatile molecules, which differentiated the three matrices. The data relating to the content of cannabinoids were also interesting as they showed that one of the three cultivars was richer than the others. Finally, an in-depth statistical survey was performed to better compare the investigated samples and identify the molecules that most contribute to differentiating them. The findings of this study may be useful for integrating the compositional information on C. sativa L.

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.

In Vitro Anti-Proliferative and Apoptotic Effects of Hydroxytyrosyl Oleate on SH-SY5Y Human Neuroblastoma Cells.

The antitumor activity of polyphenols derived from extra virgin olive oil and, in particular the biological activity of HTyr, has been studied extensively. However, the use of HTyr as a therapeutic agent for clinical applications is limited by its low bioavailability and rapid excretion in humans. To overcome these limitations, several synthetic strategies have been optimized to prepare lipophenols and new compounds derived from HTyr to increase lipophilicity and bioavailability. One very promising ester is hydroxytyrosyl oleate (HTyr-OL) because the chemical structure of HTyr, which is responsible for several biological activities, is linked to the monounsaturated chain of oleic acid (OA), giving the compound high lipophilicity and thus bioavailability in the cellular environment. In this study, the in vitro cytotoxic, anti-proliferative, and apoptotic induction activities of HTyr-OL were evaluated against SH-SY5Y human neuroblastoma cells, and the effects were compared with those of HTyr and OA. The results showed that the biological activity of HTyr was maintained in HTyr-OL treatments at lower dosages. In addition, the shotgun proteomic approach was used to study HTyr-OL-treated and untreated neuroblastoma cells, revealing that the antioxidant, anti-proliferative and anti-inflammatory activities of HTyr-OL were observed in the unique proteins of the two groups of samples.

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/).

A Bioinformatics Approach to Investigate Structural and Non-Structural Proteins in Human Coronaviruses.

Recent studies confirmed that people unexposed to SARS-CoV-2 have preexisting reactivity, probably due to previous exposure to widely circulating common cold coronaviruses. Such preexistent reactivity against SARS-CoV-2 comes from memory T cells that can specifically recognize a SARS-CoV-2 epitope of structural and non-structural proteins and the homologous epitopes from common cold coronaviruses. Therefore, it is important to understand the SARS-CoV-2 cross-reactivity by investigating these protein sequence similarities with those of different circulating coronaviruses. In addition, the emerging SARS-CoV-2 variants lead to an intense interest in whether mutations in proteins (especially in the spike) could potentially compromise vaccine effectiveness. Since it is not clear that the differences in clinical outcomes are caused by common cold coronaviruses, a deeper investigation on cross-reactive T-cell immunity to SARS-CoV-2 is crucial to examine the differential COVID-19 symptoms and vaccine performance. Therefore, the present study can be a starting point for further research on cross-reactive T cell recognition between circulating common cold coronaviruses and SARS-CoV-2, including the most recent variants Delta and Omicron. In the end, a deep learning approach, based on Siamese networks, is proposed to accurately and efficiently calculate a BLAST-like similarity score between protein sequences.

Homogentisic acid induces autophagy alterations leading to chondroptosis in human chondrocytes: Implications in Alkaptonuria.

Alkaptonuria (AKU) is an ultra-rare genetic disease caused by a deficient activity of the enzyme homogentisate 1,2-dioxygenase (HGD) leading to the accumulation of homogentisic acid (HGA) on connective tissues. Even though AKU is a multi-systemic disease, osteoarticular cartilage is the most affected system and the most damaged tissue by the disease. In chondrocytes, HGA causes oxidative stress dysfunctions, which induce a series of not fully characterized cellular responses. In this study, we used a human chondrocytic cell line as an AKU model to evaluate, for the first time, the effect of HGA on autophagy, the main homeostasis system in articular cartilage. Cells responded timely to HGA treatment with an increase in autophagy as a mechanism of protection. In a chronic state, HGA-induced oxidative stress decreased autophagy, and chondrocytes, unable to restore balance, activated the chondroptosis pathway. This decrease in autophagy also correlated with the accumulation of ochronotic pigment, a hallmark of AKU. Our data suggest new perspectives for understanding AKU and a mechanistic model that rationalizes the damaging role of HGA.

Multi-Omics Model Applied to Cancer Genetics.

In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.

Machine learning application for patient stratification and phenotype/genotype investigation in a rare disease.

Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of standardized data, information and methodologies to assess disease severity and progression represents a common complication in ultra-rare disorders like AKU. This is the reason why we developed a comprehensive tool, called ApreciseKUre, able to collect AKU patients deriving data, to analyse the complex network among genotypic and phenotypic information and to get new insight in such multi-systemic disease. By taking advantage of the dataset, containing the highest number of AKU patient ever considered, it is possible to apply more sophisticated computational methods (such as machine learning) to achieve a first AKU patient stratification based on phenotypic and genotypic data in a typical precision medicine perspective. Thanks to our sufficiently populated and organized dataset, it is possible, for the first time, to extensively explore the phenotype-genotype relationships unknown so far. This proof of principle study for rare diseases confirms the importance of a dedicated database, allowing data management and analysis and can be used to tailor treatments for every patient in a more effective way.

Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria.

ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. It includes genetic, biochemical, histopathological, clinical, therapeutic resources and quality of life scores that can be shared among registered researchers and clinicians in order to create a Precision Medicine Ecosystem (PME). The combination of machine learning application to analyse and re-interpret data available in the ApreciseKUre shows the potential direct benefits to achieve patient stratification and the consequent tailoring of care and treatments to a specific subgroup of patients. In this study, we have developed a tool able to investigate the most suitable treatment for AKU patients in accordance with their Quality of Life scores, which indicates changes in health status before/after the assumption of a specific class of drugs. This fact highlights the necessity of development of patient databases for rare diseases, like ApreciseKUre. We believe this is not limited to the study of AKU, but it represents a proof of principle study that could be applied to other rare diseases, allowing data management, analysis, and interpretation.

Homogentisic acid induces cytoskeleton and extracellular matrix alteration in alkaptonuric cartilage.

Alkaptonuria (AKU) is an ultra-rare disease caused by the deficient activity of homogentisate 1,2-dioxygenase enzyme, leading the accumulation of homogentisic acid (HGA) in connective tissues implicating the formation of a black pigmentation called "ochronosis." Although AKU is a multisystemic disease, the most affected tissue is the articular cartilage, which during the pathology appears to be highly damaged. In this study, a model of alkaptonuric chondrocytes and cartilage was realized to investigate the role of HGA in the alteration of the extracellular matrix (ECM). The AKU tissues lost its architecture composed of collagen, proteoglycans, and all the proteins that characterize the ECM. The cause of this alteration in AKU cartilage is attributed to a degeneration of the cytoskeletal network in chondrocytes caused by the accumulation of HGA. The three cytoskeletal proteins, actin, vimentin, and tubulin, were analyzed and a modification in their amount and disposition in AKU chondrocytes model was identified. Cytoskeleton is involved in many fundamental cellular processes; therefore, the aberration in this complex network is involved in the manifestation of AKU disease.

A proteomics approach to further highlight the altered inflammatory condition in Rett syndrome.

Rett syndrome (RTT) is a progressive neurodevelopmental disorder caused by mutations in the X-linked MECP2 gene. RTT patients show multisystem disturbances associated with perturbed redox homeostasis and inflammation, which appear as possible key factors in RTT pathogenesis. In this study, using primary dermal fibroblasts from control and RTT subjects, we performed a proteomic analysis that, together with data mining approaches, allowed us to carry out a comprehensive characterization of RTT cellular proteome. Functional and pathway enrichment analyses showed that differentially expressed proteins in RTT were mainly enriched in biological processes related to immune/inflammatory responses. Overall, by using proteomic data mining as supportive approach, our results provide a detailed insight into the molecular pathways involved in RTT immune dysfunction that, causing tissue and organ damage, can increase the vulnerability of affected patients to unknown endogenous factors or infections.

Amyloid-ß Precursor Protein APP Down-Regulation Alters Actin Cytoskeleton-Interacting Proteins in Endothelial Cells.

The amyloid-ß precursor protein (APP) is a ubiquitous membrane protein often associated with Alzheimer's disease (AD) and cerebral amyloid angiopathy (CAA). Despite its role in the development of the pathogenesis, APP exerts several physiological roles that have been mainly investigated in neuronal tissue. To date, the role of APP in vasculature and endothelial cells has not been fully elucidated. In this study, we used molecular and proteomic approaches to identify and investigate major cellular targets of APP down-regulation in endothelial cells. We found that APP is necessary for endothelial cells proliferation, migration and adhesion. The loss of APP alters focal adhesion stability and cell-cell junctions' expression. Moreover, APP is necessary to mediate endothelial response to the VEGF-A growth factor. Finally, we document that APP propagates exogenous stimuli and mediates cellular response in endothelial cells by modulating the Scr/FAK signaling pathway. Thus, the intact expression and processing of APP is required for normal endothelial function. The identification of molecular mechanisms responsible for vasoprotective properties of endothelial APP may have an impact on clinical efforts to preserve and protect healthy vasculature in patients at risk of the development of cerebrovascular disease and dementia including AD and CAA.

AKUImg: A database of cartilage images of Alkaptonuria patients.

ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. We present an ApreciseKUre plugin, called AKUImg, dedicated to the storage and analysis of AKU histopathological slides, in order to create a Precision Medicine Ecosystem (PME), where images can be shared among registered researchers and clinicians to extend the AKU knowledge network. AKUImg includes a new set of AKU images taken from cartilage tissues acquired by means of a microscopic technique. The repository, in accordance to ethical policies, is publicly available after a registration request, to give to scientists the opportunity to study, investigate and compare such precious resources. AKUImg is also integrated with a preliminary but accurate predictive system able to discriminate the presence/absence of AKU by comparing histopatological affected/control images. The algorithm is based on a standard image processing approach, namely histogram comparison, resulting to be particularly effective in performing image classification, and constitutes a useful guide for non-AKU researchers and clinicians.

Proteomic profiling reveals mitochondrial alterations in Rett syndrome.

Rett syndrome (RTT) is a pervasive neurodevelopmental disorder associated with mutation in MECP2 gene. Despite a well-defined genetic cause, there is a growing consensus that a metabolic component could play a pivotal role in RTT pathophysiology. Indeed, perturbed redox homeostasis and inflammation, i.e. oxinflammation, with mitochondria dysfunction as the central hub between the two phenomena, appear as possible key contributing factors to RTT pathogenesis and its clinical features. While these RTT-related changes have been widely documented by transcriptomic profiling, proteomics studies supporting these evidences are still limited. Here, using primary dermal fibroblasts from control and patients, we perform a large-scale proteomic analysis that, together with data mining approaches, allow us to carry out the first comprehensive characterization of RTT cellular proteome, showing mainly changes in expression of proteins involved in the mitochondrial network. These findings parallel with an altered expression of key mediators of mitochondrial dynamics and mitophagy associated with abnormal mitochondrial morphology. In conclusion, our proteomic analysis confirms the pathological relevance of mitochondrial dysfunction in RTT pathogenesis and progression.

Machine learning application for development of a data-driven predictive model able to investigate quality of life scores in a rare disease.

BACKGROUND: Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase (HGD) gene. One of the main obstacles in studying AKU, and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Quality of Life scores (QoL) are a reliable way to monitor patients' clinical condition and health status. QoL scores allow to monitor the evolution of diseases and assess the suitability of treatments by taking into account patients' symptoms, general health status and care satisfaction. However, more comprehensive tools to study a complex and multi-systemic disease like AKU are needed. In this study, a Machine Learning (ML) approach was implemented with the aim to perform a prediction of QoL scores based on clinical data deposited in the ApreciseKUre, an AKU- dedicated database. METHOD: Data derived from 129 AKU patients have been firstly examined through a preliminary statistical analysis (Pearson correlation coefficient) to measure the linear correlation between 11 QoL scores. The variable importance in QoL scores prediction of 110 ApreciseKUre biomarkers has been then calculated using XGBoost, with K-nearest neighbours algorithm (k-NN) approach. Due to the limited number of data available, this model has been validated using surrogate data analysis. RESULTS: We identified a direct correlation of 6 (age, Serum Amyloid A, Chitotriosidase, Advanced Oxidation Protein Products, S-thiolated proteins and Body Mass Index) out of 110 biomarkers with the QoL health status, in particular with the KOOS (Knee injury and Osteoarthritis Outcome Score) symptoms (Relative Absolute Error (RAE) 0.25). The error distribution of surrogate-model (RAE 0.38) was unequivocally higher than the true-model one (RAE of 0.25), confirming the consistency of our dataset. Our data showed that inflammation, oxidative stress, amyloidosis and lifestyle of patients correlates with the QoL scores for physical status, while no correlation between the biomarkers and patients' mental health was present (RAE 1.1). CONCLUSIONS: This proof of principle study for rare diseases confirms the importance of database, allowing data management and analysis, which can be used to predict more effective treatments.

Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.

Alkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2-dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum amyloid A (SAA) levels, which are linked with inflammation and oxidative stress and might contribute to disease progression and patients' poor quality of life. Recently, we reported that inflammatory markers (SAA and chitotriosidase) and oxidative stress markers (protein thiolation index) might be disease activity markers in AKU. Thanks to an international network, we collected genotypic, phenotypic, and clinical data from more than 200 patients with AKU. These data are currently stored in our AKU database, named ApreciseKUre. In this work, we developed an algorithm able to make predictions about the oxidative status trend of each patient with AKU based on 55 predictors, namely circulating HGA, body mass index, total cholesterol, SAA, and chitotriosidase. Our general aim is to integrate the data of apparently heterogeneous patients with AKUAKU by using specific bioinformatics tools, in order to identify pivotal mechanisms involved in AKU for a preventive, predictive, and personalized medicine approach to AKU.-Cicaloni, V., Spiga, O., Dimitri, G. M., Maiocchi, R., Millucci, L., Giustarini, D., Bernardini, G., Bernini, A., Marzocchi, B., Braconi, D., Santucci, A. Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.