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

Structural investigation of Rett-inducing MeCP2 mutations

X-ray structure of methyl-CpG binding domain (MBD) of MeCP2, an intrinsically disordered protein (IDP) involved in Rett syndrome, offers a rational basis for defining the spatial distribution for most of the sites where mutations responsible of Rett syndrome, RTT, occur. We have ascribed pathogenicity for mutations of amino acids bearing positively charged side chains, all located at the protein-DNA interface, as positive charge removal cause reduction of the MeCP2-DNA adduct lifetime. Pathogenicity of the frequent proline replacements, outside the DNA contact moiety of MBD, can be attributed to the role of this amino acid for maintaining both unfolded states for unbound MeCP2 and, at the same time, to favor some higher conformational order for stabilizing structural determinants required by protein activity. These hypotheses can be extended to transcription repressor domain, TRD, the other MeCP2-DNA interaction site and, in general, to all the IDP that interact with nucleic acids

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

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

AKUImg: A database of cartilage images of Alkaptonuria patients

ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis forpatients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. We present anApreciseKUre 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 registeredresearchers and clinicians to extend the AKU knowledge network. AKUImg includes a new set of AKU imagestaken from cartilage tissues acquired by means of a microscopic technique. The repository, in accordanceto ethical policies, is publicly available after a registration request, to give to scientists the opportunity tostudy, investigate and compare such precious resources. AKUImg is also integrated with a preliminary butaccurate predictive system able to discriminate the presence/absence of AKU by comparing histopatologicalaffected/control images. The algorithm is based on a standard image processing approach, namely histogramcomparison, resulting to be particularly effective in performing image classification, and constitutes a usefulguide for non-AKU researchers and clinician

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

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

Homogentisate 1,2-dioxygenase (HGD) gene variants, their analysis and genotype–phenotype correlations in the largest cohort of patients with AKU

Alkaptonuria (AKU) is a rare metabolic disorder caused by a deficient enzyme in the tyrosine degradation pathway, homogentisate 1,2-dioxygenase (HGD). In 172 AKU patients from 39 countries, we identified 28 novel variants of the HGD gene, which include three larger genomic deletions within this gene discovered via self-designed multiplex ligation-dependent probe amplification (MLPA) probes. In addition, using a reporter minigene assay, we provide evidence that three of eight tested variants potentially affecting splicing cause exon skipping or cryptic splice-site activation. Extensive bioinformatics analysis of novel missense variants, and of the entire HGD monomer, confirmed mCSM as an effective computational tool for evaluating possible enzyme inactivation mechanisms. For the first time for AKU, a genotype–phenotype correlation study was performed for the three most frequent HGD variants identified in the Suitability Of Nitisinone in Alkaptonuria 2 (SONIA2) study. We found a small but statistically significant difference in urinary homogentisic acid (HGA) excretion, corrected for dietary protein intake, between variants leading to 1% or >30% residual HGD activity. There was, interestingly, no difference in serum levels or absolute urinary excretion of HGA, or clinical symptoms, indicating that protein intake is more important than differences in HGD variants for the amounts of HGA that accumulate in the body of AKU patients