Annamaria Vernone

The search for the biomarkers of Alzheimer’s disease (AD) may prove essential in the diagnosis and prognosis of the pathology, and the differential expression of key proteins may assist in identifying new therapeutic targets. In this proof-of-concept (POC) study, a new approach of data mining and matching combined with the biochemical analysis of proteins was applied to AD investigation. Three influential online open databases (UniProt, AlzGene, and Allen Human Brain Atlas) were explored to identify the genes and encoded proteins involved in AD linked to mitochondrial and iron dysmetabolism. The databases were searched using specific keywords to collect information about protein composition, and function, and meta-analysis data about their correlation with AD. The extracted datasets were matched to yield a list of relevant proteins in AD. The biochemical analysis of their amino acid content suggested a defective synthesis of these proteins in poorly oxygenated brain tissue, supporting their relevance in AD progression. The result of our POC study revealed several potential new markers of AD that deserve further molecular and clinical investigation. This novel database search approach can be a valuable strategy for biomarker search that can be exploited in many diseases.

Iron overload in many brain regions is a common feature of aging and most neurodegenerative diseases. In this review, the causes, mechanisms, mathematical models, and possible therapies are summarized. Indeed, physiological and pathological conditions can be investigated using compartmental models mimicking iron trafficking across the blood–brain barrier and the Cerebrospinal Fluid-Brain exchange membranes located in the choroid plexus. In silico models can investigate the alteration of iron homeostasis and simulate iron concentration in the brain environment, as well as the effects of intracerebral iron chelation, determining potential doses and timing to recover the physiological state. Novel formulations of non-toxic nanovectors with chelating capacity are already tested in organotypic brain models and could be available to move from in silico to in vivo experiments.

SARS-CoV-2 induces a broad range of clinical manifestations. Besides the main receptor, ACE2, other putative receptors and co-receptors have been described and could become genuinely relevant to explain the different tropism manifested by new variants. In this study, we propose a biochemical model envisaging the competition for cysteine as a key mechanism promoting the infection and the selection of host receptors. The SARS-CoV-2 infection produces ROS and triggers a massive biosynthesis of proteins rich in cysteine; if this amino acid becomes limiting, glutathione levels are depleted and cannot control oxidative stress. Hence, infection succeeds. A receptor should be recognized as a marker of suitable intracellular conditions, namely the full availability of amino acids except for low cysteine. First, we carried out a comparative investigation of SARS-CoV-2 proteins and human ACE2. Then, using hierarchical cluster protein analysis, we searched for similarities between all human proteins and spike produced by the latest variant, Omicron BA.1. We found 32 human proteins very close to spike in terms of amino acid content. Most of these potential SARS-CoV-2 receptors have less cysteine than spike. We suggest that these proteins could signal an intracellular shortage of cysteine, predicting a burst of oxidative stress when used as viral entry mediators.

Notwithstanding the huge amount of detailed information available in protein databases, it is not possible to automatically download a list of proteins ordered by the position of their codifying gene. This order becomes crucial when analyzing common features of proteins produced by loci or other specific regions of human chromosomes. In this study, we developed a new procedure that interrogates two human databases (genomic and protein) and produces a novel dataset of ordered proteins following the mapping of the corresponding genes. We validated and implemented the procedure to create a user-friendly web application. This novel data mining was used to evaluate the distribution of critical amino acid content in proteins codified by a human chromosome. For this purpose, we designed a new methodological approach called chromosome walking, which scanned the whole chromosome and found the regions producing proteins enriched in a selected amino acid. As an example of biomedical application, we investigated the human chromosome 15, which contains the locus DYX1 linked to developmental dyslexia, and we found three additional putative gene clusters whose expression could be driven by the environmental availability of glutamate. The novel data mining procedure and analysis could be exploited in the study of several human pathologies.

The novel COVID-19 pandemic is affecting the world’s population differently: mostly in the presence of conditions such as aging, diabetes and hypertension the virus triggers a lethal cytokine storm and patients die from acute respiratory distress syndrome, whereas in many cases the disease has a mild or even asymptomatic progression. A common denominator in all conditions associated with COVID-19 appears to be the impaired redox homeostasis responsible for reactive oxygen species (ROS) accumulation; therefore, levels of glutathione (GSH), the key anti-oxidant guardian in all tissues, could be critical in extinguishing the exacerbated inflammation that triggers organ failure in COVID-19. The present review provides a biochemical investigation of the mechanisms leading to deadly inflammation in severe COVID-19, counterbalanced by GSH. The pathways competing for GSH are described to illustrate the events concurring to cause a depletion of endogenous GSH stocks. Drawing on evidence from literature that demonstrates the reduced levels of GSH in the main conditions clinically associated with severe disease, we highlight the relevance of restoring GSH levels in the attempt to protect the most vulnerable subjects from severe symptoms of COVID-19. Finally, we discuss the current data about the feasibility of increasing GSH levels, which could be used to prevent and subdue the disease.

In this study, we investigated whether the relative abundance of glutamate and glutamine in human proteins reflects the availability of these amino acids (AAs) dictated by the cellular context. In particular, because hypoxia increases the conversion of glutamate to glutamine, we hypothesized that the ratio glutamate/glutamine could be related to tissue oxygenation. By histological, biochemical and genetic evaluation, we identified proteins expressed selectively by distinct cellular populations that are exposed in the same tissue to high or low oxygenation, or proteins codified by different chromosomal loci. Our biochemical assessment was implemented by software tools that calculated the absolute and the relative frequencies of all AAs contained in the proteins. Moreover, an agglomerative hierarchical cluster analysis was performed. In the skin model that has a strictly local metabolism, we demonstrated that the ratio glutamate/glutamine of the selected proteins was directly proportional to oxygenation. Accordingly, the proteins codified by the epidermal differentiation complex in the region 1q21.3 and by the lipase clustering region 10q23.31 showed a significantly lower ratio glutamate/glutamine compared with the nearby regions of the same chromosome. Overall, our results demonstrate that the estimation of glutamate/glutamine ratio can give information on tissue oxygenation and could be exploited as marker of hypoxia, a condition common to several pathologies.

The paper focuses on the development of a software tool for protein clustering according to their amino acid content. All known human proteins were clustered according to the relative frequencies of their amino acids starting from the UniProtKB/Swiss-Prot reference database and making use of hierarchical cluster analysis. Results were compared to those based on sequence similarities. Results: Proteins display different clustering patterns according to type. Many extracellular proteins with highly specific and repetitive sequences (keratins, collagens etc.) cluster clearly confirming the accuracy of the clustering method. In our case clustering by sequence and amino acid content overlaps. Proteins with a more complex structure with multiple domains (catalytic, extracellular, transmembrane etc.), even if classified very similar according to sequence similarity and function (aquaporins, cadherins, steroid 5-alpha reductase etc.) showed different clustering according to amino acid content. Availability of essential amino acids according to local conditions (starvation, low or high oxygen, cell cycle phase etc.) may be a limiting factor in protein synthesis, whatever the mRNA level. This type of protein clustering may therefore prove a valuable tool in identifying so far unknown metabolic connections and constraints.

This paper presents a method for blind digital watermarking of color images. The proposed scheme performs a decorrelation of the RGB color bands of the image to be watermarked using a Karhunen–Loève transform (KLT), then marks the coefficients of the KLT of the first band obtained. The novelty of the method resides in the fact that the KLT basis images used for the transforms are not related to the image being marked, but are from an image taken as the secret key used throughout the scheme. The watermarking of the chosen KLT coefficients is performed using an i.i.d. Gaussian sequence. The method shows good robustness to attacks and signal processing operations, in particular to JPEG compression, filtering, resampling and noise addition, maintaining at the same time a high image quality. In addition, the scheme is secure against protocol attacks such as copy attack and ambiguity attack.

In this paper, a block-based watermarking scheme based on the Singular Value Decomposition (SVD) is proposed. Our watermark, a pseudo-random Gaussian sequence, is embedded by modifying the angles formed by the right singular vectors of each block of the original image. The orthogonality property of the right singular vector matrix is preserved during the embedding process. Several experiments have been carried out to test the performance of the proposed scheme against different attack scenarios. We conclude that the proposed scheme is resistant against common signal processing operations and attacks, while it preserves the quality of the original image.