A molecular spectroscopy approach for the investigation of early phase ochronotic pigment development in Alkaptonuria

HGA Triggers SAA Aggregation and Accelerates Fibril Formation in the C20/A4 Alkaptonuria Cell Model

Alkaptonuria (AKU) is a rare autosomal recessive metabolic disorder caused by mutations in the homogentisate 1,2-dioxygenase (HGD) gene, leading to the accumulation of homogentisic acid (HGA), causing severe inflammatory conditions. Recently, the presence of serum amyloid A (SAA) has been reported in AKU tissues, classifying AKU as novel secondary amyloidosis; AA amyloidosis is characterized by the extracellular tissue deposition of fibrils composed of fragments of SAA. AA amyloidosis may complicate several chronic inflammatory conditions, like rheumatoid arthritis, ankylosing spondylitis, inflammatory bowel disease, chronic infections, neoplasms, etc. Treatments of AA amyloidosis relieve inflammatory disorders by reducing SAA concentrations; however, no definitive therapy is currently available. SAA regulation is a crucial step to improve AA secondary amyloidosis treatments. Here, applying a comprehensive in vitro and in silico approach, we provided evidence that HGA is a disruptor modulator of SAA, able to enhance its polymerization, fibril formation, and aggregation upon SAA/SAP colocalization. In silico studies deeply dissected the SAA misfolding molecular pathway and SAA/HGA binding, suggesting novel molecular insights about it. Our results could represent an important starting point for identifying novel therapeutic strategies in AKU and AA secondary amyloidosis-related diseases.

The structural complexity of pyomelanin impacts UV shielding in Pseudomonas species with different lifestyles

Pyomelanin, a polymeric pigment in Pseudomonas, arises mainly from alterations in tyrosine degradation. The chemical structure of pyomelanin remains elusive due to its heterogeneous nature. Here, we report strain-specific differences in pyomelanin structural features across Pseudomonas using PAO1 and PA14 reference strains carrying mutations in hmgA (a gene involved in pyomelanin synthesis), a melanogenic P. aeruginosa clinical isolate (PAM), and a melanogenic P. extremaustralis (PexM). UV spectra showed dual peaks for PAO1 and PA14 mutants and single peaks for PAM and PexM. FTIR phenol : alcohol ratio changes and complex NMR spectra indicated non-linear polymers. UVC radiation survival increased with pyomelanin addition, correlating with pigment absorption attenuation. P. extremaustralis UVC survival varied with melanin source, with PAO1 pyomelanin being the most protective. These findings delineate structure-based pyomelanin subgroups, having distinct physiological effects.

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.

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.

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.

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.

Survey on the Recent Advances in 4-Hydroxyphenylpyruvate Dioxygenase (HPPD) Inhibition by Diketone and Triketone Derivatives and Congeneric Compounds: Structural Analysis of HPPD/Inhibitor Complexes and Structure-Activity Relationship Considerations

The serendipitous discovery of the HPPD inhibitors from allelopathic plants opened the way for searching new and effective herbicidal agents by application of classical hit-to-lead optimization approaches. A plethora of active and selective compounds were discovered that belong to three major classes of cyclohexane-based triketones, pyrazole-based diketones, and diketonitriles. In addition, to enhance inhibitory constant and herbicidal activity, many efforts were also made to gain broader weed control, crop safety, and eventual agricultural applicability. Moreover, HPPD inhibitors emerged as therapeutic agents for inherited and metabolic human diseases as well as vector-selective insecticides in the control of hematophagous arthropods. Given the large set of experimental data available, structure-activity relationship analysis could be used to derive suggestions for next generation optimized compounds.

Computational Approaches Integrated in a Digital Ecosystem Platform for a Rare Disease

Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase 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. Based on that, a multi-purpose digital platform, called ApreciseKUre, was implemented to facilitate data collection, integration and analysis for patients affected by AKU. It includes genetic, biochemical, histopathological, clinical, therapeutic resources and Quality of Life (QoL) scores that can be shared among registered researchers and clinicians to create a Precision Medicine Ecosystem. The combination of machine learning applications to analyse and re-interpret data available in the ApreciseKUre clearly indicated the potential direct benefits to achieve patients' stratification and the consequent tailoring of care and treatments to a specific subgroup of patients. In order to generate a comprehensive patient profile, computational modeling and database construction support the identification of potential new biomarkers, paving the way for more personalized therapy to maximize the benefit-risk ratio. In this work, different Machine Learning implemented approaches were described.